Mlopspekerjaan

...with the core orchestrator, no third part tool. • Web navigation/ scraping with Selenium/Playwright: document download, classification, OCR/text extraction. • Build/train neural networks (e.g., CNNs for image doc classification). • NLP expertise with spaCy for entity extraction. • Computer vision using TensorFlow/OpenCV (offline Vision Libraries preferred). Preferred Skills: • MLOps (e.g., MLflow, Docker for deployment). • Strong problem-solving for complex, error-prone workflows. • 2+ years portfolio with RPA/CV projects (GitHub links required). Project Details: • Milestones: Week 4 (scraper prototype), Week 8 (CV model), Week 12 (full RPA pipeline). • Tools: Python 3.10+, Git, Jupyter. Patient, met...

...systems (TTS, STT, STS) Experience in LLM fine-tuning, quantization, and model optimization Ability to deploy self-hosted / offline AI models Strong backend development skills (Python, Node.js, FastAPI, Django, etc.) Experience designing scalable, modular backend architectures Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, etc.) Understanding of cloud, containers, and MLOps (Docker, Kubernetes is a plus)...

I need an experienced AI/ML freelancer to take the lead on building a custom copilot that streamlines day-to-day clinical and administrative workflows for a healthcare platform. The immediate goal is to move from scattered manual steps to an intelligent...Slack/Teams bot • At least three predefined healthcare workflows automated end-to-end (e.g., visit note drafting, prior-auth request, lab follow-up) • Clear README with setup, environment variables, and model/embedding choices • Security checklist confirming HIPAA-ready data handling and access controls Once the copilot’s core loop is stable, we can expand into fine-tuning, evaluation, and full MLOps deployment, but the focus right now is that initial working assistant. I’m ready to start as soon a...

...enforcing context bound generation and preventing hallucination outside retrieved evidence. Indexing is parallelized using ProcessPoolExecutor for efficient multi core utilization and automatically scales to distributed ingestion via PySpark when corpus size exceeds a configured threshold, enabling safe handling of 20k plus documents or 50GB class corpora, while the system is wrapped in a full MLOps backbone that integrates MLflow for experiment tracking of retrieval metrics, PPO reinforcement learning rewards, and parameter tuning, exposes Prometheus metrics for latency and retrieval monitoring compatible with Grafana dashboards, and supports Airflow DAG orchestration for scheduled indexing and policy training workflows. Reinforcement learning is implemented using a PyTorch base...

Azure MLOps Trainer Needed for Training Sessions We are hiring a senior Azure AI engineer to provide structured, hands-on training in building enterprise-grade LLM systems. This is NOT a beginner AI course and NOT a chatbot project. We need practical training in implementing: - Azure OpenAI API integrations (production-ready) - Full RAG pipelines using Azure AI Search (vector + hybrid search) - Document ingestion workflows (Blob > OCR > chunking > embeddings) - Function/tool-calling for agentic workflows - Secure deployment using Azure Functions / Container Apps / AKS - Logging, retries, structured validation, and reliability patterns - Enterprise constraints (RBAC, private endpoints, managed identity) The focus is: - Clean architecture - Production patterns - Observabi...

Senior AI / ML Architect – GenAI, MLOps & Enterprise AI Work Support (10+ Years) Job Description We are seeking a highly experienced AI/ML professional (10+ years) to provide ongoing technical work support across advanced AI, GenAI, and data-driven systems. This role involves hands-on guidance, design reviews, problem-solving, and production support for complex AI/ML implementations in enterprise environments. The ideal candidate has deep real-world experience and can quickly understand requirements, identify gaps, and provide clear technical direction. Candidates may specialize in any subset of the skills listed below. Core Expertise (Any of the Below) Generative AI & LLM Systems LLM-based applications and enterprise GenAI platforms Prompt design, alignment, ...

...automatically scouts freelancing websites, general job boards, and specialised training platforms for roles or courses that involve artificial-intelligence work. The agent must: • Crawl and scrape the relevant pages in real time or on a frequent schedule. • Apply NLP or other classification techniques to decide whether a posting is truly AI-related, then tag it by sub-domain (e.g. vision, NLP, MLOps, prompt-engineering). • Deliver concise, deduplicated listings to me through an in-app notification feed—no email or SMS required. For the deployment side I’m open to Python (Scrapy, BeautifulSoup, Selenium), Node, or any stack you are comfortable with so long as it is containerised and can run unattended on a small cloud instance. A lightweight web in...

...criteria A working API must return real-time predictions within agreed latency limits, integrate seamlessly with the current SMTP/ESP workflow, and include logging for compliance review. Final delivery is considered complete when the system runs in production and all documentation passes peer review. Tools & stack Python, scikit-learn or TensorFlow, SQL/NoSQL for data storage, and standard MLOps utilities (Docker, CI/CD) are anticipated, yet alternative libraries are welcome if they meet the same reliability and security standards. Timeline and milestones will be outlined together at project start, with code reviews scheduled at each major checkpoint....

...model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding is essential; I want clear explanations of feature importance, assumptions and limitations that business stakeholders can grasp quickly. Big-data exposure, cloud familiarity (Azure, AWS or GCP), ETL pipeline design and MLOps practices are all welcome extras—you’ll have room to propose improvements if they make the solution more robust or scalable. Deliverables I need from you: • A well-documented predictive model with reproducible code and clear version control • Cleaned and transformed datasets stored back into SQL (or a recommended alternative) • An interactive Pow...

...Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with REST APIs and Microservices • Excellent analytical and communication skills • Experience with Generative AI and LLM frameworks • Knowledge of Docker, Kubernetes • ...

...Required Qualifications 8+ years of experience as an AI Architect or similar role, with proven expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, GPT models, RAG systems). Strong background in bridging business needs to technical implementations, demonstrated through successful projects with short development cycles. Proficiency in cloud platforms (AWS, Azure, GCP) for AI deployment, MLOps, and scalable architectures. Experience in data engineering, NLP, computer vision, or predictive modeling relevant to business applications. Excellent communication skills to articulate complex AI concepts to non-technical stakeholders. Bachelor's or Master's degree in Computer Science, AI, or a related field. Preferred Qualifications Prior experience in travel, hospitalit...

...systems (TTS, STT, STS) Experience in LLM fine-tuning, quantization, and model optimization Ability to deploy self-hosted / offline AI models Strong backend development skills (Python, Node.js, FastAPI, Django, etc.) Experience designing scalable, modular backend architectures Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, etc.) Understanding of cloud, containers, and MLOps (Docker, Kubernetes is a plus)...

...best practices. Required Skills Strong understanding of AI/ML concepts such as: Predictive analytics, forecasting, classification, NLP/LLM, GenAI, RPA, model evaluation, etc. Ability to translate business problems into AI/ML use cases. Ability to communicate complex technical concepts to non-technical audiences. Familiarity with modern data platforms, cloud providers (AWS/Azure/GCP), and MLOps practices. Experience in proposal creation, solution pitching, and pre-sales cycles. Acceptance Criteria Minimum 5 years of experience in AI/ML solutioning, consulting, or presales. Experience leading client discussions and presenting solutions. Demonstrated ability to define business use cases and value propositions. Exposure to enterprise customers or large-scale business tran...

...while ensuring compliance, deliverability, and scalability. We are deliberately not prescribing tools. You are expected to choose the right architecture and tooling based on the requirements below. What We Need Built (Outcomes) 1. Demand Signal Detection System: Design a system that can automatically identify companies that are actively hiring for roles such as LLM Trainers, AI / ML Engineers, MLOps Engineers, Model Evaluators, AI Researchers. The system should prioritize high-intent signals, such as recent job postings, repeated hiring for similar roles, active recruiter or TA hiring activity, output should be a daily, refreshed list of qualified companies 2. Account & Contact Mapping: For each qualified company, the system should: - Identify the right decision-layer co...

...suggested timings. Packaged folder: PPTX/Google Slides, PDF handouts, images, quiz spreadsheet, rubric files. Courses to produce (high level) Data Literacy & Governance (Dummy→Hero, templates for audit, DPIA, stewardship) AI Literacy, Governance & Security (model audit, privacy for ML, incident playbooks) AI Mastery — Advanced (deep dives: evaluation, interpretability, advanced privacy, production MLOps best practices, research-to-production workflows) AI Graphics & Video Editing (practical: prompt engineering for visuals/video, workflows for AI-assisted editing, tools pipeline, export-ready assets) Required skills & experience (must have) Instructional design for adult learners (tech/enterprise audiences). Strong slide-deck design skills &mdas...

...with? How do you typically define and defend novelty and contribution in applied research? Describe your experience responding to reviewer comments. Give a sample of Similar Books you have published and publishers you worked with ======================================= The works are based on original material (slides, transcripts, frameworks) focused on AI infrastructure, data centers, energy, MLOps, and applied AI economics. additional projects in clude -AI Literacy, Governance & Security; Data Literacy & Governance This is not marketing content or SEO writing. We are looking for someone who understands peer-reviewed publishing, scholarly contribution, and editorial positioning. Scope of Work Journal Paper Shape a journal-ready manuscript aligned to Q1/Q2 journals ...

...devoted to pushing the limits of artificial intelligence, and I’m ready to bring a committed AI software engineer into the core team. You will own the software layer of our AI stack—designing training pipelines, shaping inference services, integrating state-of-the-art models, and turning ambitious ideas into production-ready code. Expect to work hands-on with Python, PyTorch or TensorFlow, modern MLOps tooling (Docker, Kubernetes, CI/CD), and a cloud platform such as AWS or GCP. Because we’re still small, your voice will matter. Whether your strongest suit is classic machine learning, NLP, computer vision, or another specialty, it’s the ability to turn theory into robust, well-tested code that counts. Deliverables • An initial proof-of-concept ser...

...passionate about solving real-world problems using AI and security-driven approaches. I enjoy working at the intersection of machine learning, cybersecurity, and data science, with a particular interest in secure machine learning systems, threat detection, and intelligent data modeling. Key Skills: Machine Learning, Cybersecurity, Cryptography, Data Analysis, Statistical Modeling, Python, Java, C, SQL, MLops (Airflow, Mlflow, Docker, FastAPI), Forensics Tools, Network Security (Wireshark), SPSS, SAS, R....

...Landing) using VideoMAE or TimeSformer. Data Correlation: Translate biomechanical outputs into performance scores and predictive potential markers. Required Technical Stack Languages/Frameworks: Python, PyTorch (Advanced). Vision: YOLO v8-v11, RT-DETR, Ultralytics, DeepLabCut, SLEAP. Spatio-Temporal: VideoMAE, Video Swin Transformer, TimeSformer. Deployment/Optimization: ONNX Runtime, TensorRT, MLOps (Weights & Biases / MLflow). Mandatory Candidate Qualifications We are looking for a top-tier expert. Please only apply if you meet the following criteria: Serious References: You must provide verifiable case studies or GitHub repositories of similar complex computer vision projects (pose estimation, motion analysis, or animal tracking). Experience: At least 5+ years of...

...Landing) using VideoMAE or TimeSformer. Data Correlation: Translate biomechanical outputs into performance scores and predictive potential markers. Required Technical Stack Languages/Frameworks: Python, PyTorch (Advanced). Vision: YOLO v8-v11, RT-DETR, Ultralytics, DeepLabCut, SLEAP. Spatio-Temporal: VideoMAE, Video Swin Transformer, TimeSformer. Deployment/Optimization: ONNX Runtime, TensorRT, MLOps (Weights & Biases / MLflow). Mandatory Candidate Qualifications We are looking for a top-tier expert. Please only apply if you meet the following criteria: Serious References: You must provide verifiable case studies or GitHub repositories of similar complex computer vision projects (pose estimation, motion analysis, or animal tracking). Experience: At least 5+ years of...

Key Responsibilities MLOps Responsibilities: Collaborate with data scientists to operationalize ML workflows. Build complete ML pipelines with Airflow, Kubeflow Pipelines, or Metaflow. Deploy models using KServe, Seldon Core, BentoML, TorchServe, or TF Serving. Package models into Docker containers using Flask or FastAPI or Django for APIs. Automated dataset versioning & model tracking via DVC and MLflow. Setup model registries and ensure reproducibility and audit trails. Implement model monitoring for: (i) Data drift and schema validation (using tools like Evidently AI, Alibi Detect). (ii) Performance metrics (accuracy, precision, recall). (iii) Infrastructure metrics (latency, throughput, memory usage). Implement event-driven retraining workflows triggered by drift alerts ...

...agency—you’ll shape our voice and distribution based on the initial content we share. Direct access to founders + engineering. Automation and AI are core to how we work, not afterthoughts. WHO YOU’LL MARKET TO (ICP) * Fortune 500 / enterprise AI platform teams * AI-first companies (model/app builders) * Agentic companies (multi-agent products + automation) Stakeholders: Platform Eng, ML Eng/MLOps, Infra/DevOps, Data/AI leads, technical founders. We’ll provide the technical input; you’ll translate it into clear marketing. WHAT YOU’LL DO A) Collateral + messaging * One-pagers, decks, solution briefs, case studies, battlecards * Persona-based messaging + positioning updates B) Outbound (Email + LinkedIn) * Write/test sequences, iterate week...

...caching layers. ● Solid data engineering experience with Postgres, DynamoDB, and ClickHouse. ● Frontend engineering in React/ + TypeScript. NICE-TO-HAVE SKILLS ● Experience with Whisper, ElevenLabs, multimodal vision systems. ● Experience with GEPA-style prompt optimization loops. ● Experience with browser extension development for AI product workflows. ● Kubernetes familiarity and advanced MLOps methodologies. SUCCESS METRICS ● High reliability multi-agent workflows with automated recoverability. ● Efficient and deterministic RAG retrieval systems. ● Low-latency streaming architecture supporting real-time AI UI. ● Successful multi-LLM orchestration with cost reduction and fallback stability. ● Production-ready evaluation and regression testing frameworks. SUBMISSION REQUIREMENTS...

... REQUIRED TECHNICAL EXPERTISE LLM & NLP: • GPT, LLaMA, Claude, Gemini • RAG pipelines, embeddings, summarization Voice AI: • TTS (Azure, ElevenLabs, Coqui) • ASR (Whisper, NeMo) • SSML, voice cloning, audio DSP Video & Avatar AI: • FFmpeg automation, OpenCV, Whisper • Wav2Lip, SyncNet, avatar generation (D-ID, Synthesia) Computer Vision: • YOLO models, segmentation, OCR, moderation filters MLOps & Architecture: • Kubernetes, Docker, FastAPI • Model serving (Triton, TorchServe) • Vector DBs (Pinecone, Weaviate, FAISS) • Airflow, Temporal, CI/CD Backend Systems: • Distributed systems, microservices • REST & WebSocket services • AWS/GCP/Azure infrastructure PREFERRED QUALIFICATIONS &bull...

Phase I: Architecture Design and SetupThis phase establishes the core infrastructure for scalability and security. 1.1. Backend Infrastructure Setup (B1, B5, B9)Select and provision cloud resources (e.g., AWS, GCP, Azure). Set up the API gateway with load balancing. Define the /v1/liveness/verify endpoint. Set up object storage (e.g., S3, GCS) with mandatory AES-256 encr...Worker Fleet to ensure the target latency is met under peak Penetration Testing: Conduct a third-party security audit focusing on data transmission and storage Audit: Verify that the automated Data Retention Compliance mechanisms are correctly implemented and tested for scheduled : Final deployment to the production environment and transition to the MLOps monitoring phase.

...Build, maintain, and optimize MLOps pipelines for model training, deployment, monitoring, and retraining - Integrate Generative AI models into enterprise applications and workflows - Apply deep learning, neural networks, image processing, or conversational AI where needed - Act as the SME for AI/ML engineering practices, standards, and solution architecture - Collaborate with cross-functional teams to support end-to-end delivery - Ensure production systems meet reliability, scalability, and performance standards - Guide and mentor junior engineers and contribute to technical leadership - Support data engineering pipelines, data preparation, and model operationalization Required Qualifications - 5+ years of experience in AI/ML engineering, including 3+ years in MLOps - St...

I’m building an MLOps pipeline within a Non-Real-Time RAN Intelligent Controller (NON-RT RIC) environment to support intelligent network optimization. The goal is to automate the lifecycle of ML models—data ingestion, training, validation, deployment, and monitoring—using Kubeflow and KServe as core components. What I Need MLOps architecture design tailored to NON-RT RIC workflows. Integration of Kubeflow Pipelines for model training, retraining, and CI/CD automation. Configuration of KServe for scalable, production-grade model serving inside the RIC ecosystem. Assistance connecting the pipeline to network-metric sources and RIC policy output logic. Support in debugging model workflows, serving issues, containerization problems, or RIC integration gaps...

...surface and the concrete steps required to harden it from day one. You’ll help me: • Map potential threat vectors using frameworks such as MITRE ATLAS, OWASP for ML, or similar tools you trust (Adversarial Robustness Toolbox, CleverHans, Foolbox, etc.). • Produce an actionable risk-ranked report that details each vulnerability and the mitigation strategy, including any controls to embed in our MLOps pipeline. • Review (or co-create) high-level architecture diagrams, flagging weak points in data ingestion, training, inference, and model storage. • Recommend best practices for secure coding, access control, monitoring, and incident response specific to AI workloads. Acceptance criteria 1. Written assessment clearly lists each identified ris...

I’m building an Open RAN (O-RAN) solution and now need a production-ready MLOps pipeline around it. Kubeflow will orchestrate every workflow and KServe will handle model serving. The most critical pieces for me are model-training and model-deployment flows; CI/CD for the surrounding infrastructure is secondary. I already have several trained models that must be containerised and slotted straight into the new pipeline. Here’s what I expect to receive: • Infrastructure-as-code that spins up the required Kubernetes cluster on any major cloud provider, ready for Kubeflow and KServe • Kubeflow Pipelines covering data ingest, feature processing, training, validation, and artifact versioning • KServe endpoints with blue/green or canary rollout support so mo...

Szukam doświadczonego specjalisty AI/MLOps (GPU Infrastructure Engineer) do zdalnego przygotowania, optymalizacji i konfiguracji dwóch serwerów z kartami 10× RTX 3090 SuprimX. Celem projektu jest przygotowanie infrastruktury do wynajmu mocy GPU na platformach compute (, RunPod, TensorDock, SkyCompute) oraz do uruchomienia dodatkowych usług GPU (NVENC, rendering, inference). Projekt jest jednorazowy + możliwość stałej współpracy. Zakres prac – czego potrzebuję 1. Konfiguracja infrastruktury GPU (2 serwery po 5× GTX 3090) • instalacja i konfiguracja Ubuntu Server • konfiguracja sterowników NVIDIA (stabilne wersje) • instalacja CUDA 11.x / 12.x • Nvidia Container Toolkit • optymalizacja kernel, GRUB, hugepa...

We are looking for an ML/AI Engineer with strong Python skills and hands-on experience deploying machine learning models in cloud environments. Requirements Excellent proficiency in Python and experience with Jupyter/Colab. Hands-on experience with MLOps tools: model versioning, deployment/orchestration, monitoring. Experience with cloud platforms (preferably AWS) and deploying ML models into production. English level: B2 or higher. Nice to Have Experience applying ML/AI to real product tasks (NLP, LLMs, generative models). Strong understanding of model inference, optimization techniques, and production-level deployment. Experience improving model efficiency: latency, throughput, caching, batching, compression, etc. Responsibilities Designing and optimizing ML model deve...

...experience Desired Skills: Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment. Background in designing and implementing security mitigations and protections and/or publications in the space Currently participating in CTF/GRT/AI Red Teaming events and/or bug bounties developing or contributing to OSS projects. Understanding of ML lifecycle and MLOps. Perform various types of tests such as functional testing, regression testing, performance testing, and usability testing to evaluate the behavior and performance of the AI algorithms and models Ability to ensure the quality, consistency and relevance of data used for training and testing AI models (includes collecting, preprocessing and validating data) Ability to assess A...

I’m looking for a skilled engineer to create lightweight agents that can gather trace logs and key metrics across my entire stack. Scope • Cloud infrastructure: the agents must seamlessly collect data from AWS, Azure, and Google Cloud. • Containers: coverage is required for Docker, Kubernetes, and OpenShift environments. • AIOps / MLOps focus: the data pipeline has to capture performance metrics, logs and trace information, and resource-utilization figures so downstream analytic models receive complete, high-quality signals. What I need from you 1. Design and build installable agents (or sidecars) that auto-discover resources, stream data securely, and add minimal overhead. 2. Provide configuration options so I can enable or disable specific metric g...

...drawdowns, order/fill logs. * Alerts to email/Telegram/Slack. ## Nice-to-Have (Phase 2) * Options greeks & spreads; portfolio optimizer. * Alternative data (order book depth, options chain, social sentiment). * Strategy marketplace, multi-account orchestration. ## Tech Stack (Suggested) * **ML/Backend:** Python (FastAPI), Pandas/NumPy, scikit-learn, PyTorch/LightGBM, MLflow. * **Pipelines/MLOps:** Airflow/Prefect, Feast (feature store), Redis, Kafka (optional). * **DB/Storage:** PostgreSQL + TimescaleDB; object storage for artifacts. * **Frontend:** React/Next.js. * **Infra:** Docker, CI/CD, IaC (Terraform), AWS/GCP/Azure. ## Deliverables 1. Architecture + data/contracts + broker adapters. 2. **AI Algo Suite** (signals, scalping module, future-mover ranker) with noteb...

...building or consuming REST APIs at scale. - Familiarity with cloud AI tools like AWS Rekognition, Azure Face API, or Google Vision AI. - Understanding of Face Recognition fundamentals (Detection, Verification, Identification). - Comfortable working with Git and standard DevOps practices. Bonus Skills (Nice-to-Haves) - Experience with OpenCV or dlib for custom image processing. - Knowledge of MLOps or monitoring deployed AI systems. - Background in high-security or privacy-sensitive projects. - Understanding of Redis, Memcached, or similar caching systems. Why Work With Us You’ll be joining a tech-driven team working on a next-gen image tagging system powered by AI. We value clean architecture, speed, and privacy-conscious innovation — and you’ll have the fre...

...seasoned professionals preparing for advanced model-deployment interviews, so I need tutors comfortable adjusting depth on the fly. What I’m looking for • Fluency in spoken and written Telugu, plus clear English terminology when the topic demands it. • Solid, real-world experience with AI/ML workflows—think Python, Jupyter, pandas, scikit-learn, TensorFlow/PyTorch, model evaluation, and basic MLOps concepts. • The ability to design concise lesson plans, slides or notebooks, mini-projects, and quick assessments appropriate for beginner, intermediate, and advanced tracks. • A teaching style that blends theory with hands-on coding demos, live debugging, and practical case studies. Typical engagement I’ll share the student’s backgro...

We’re building an Industrial Digital Twin & Generative AI platform that connects real-world operations with intelligent conversational systems. We’re looking for an experienced AI Engineer to design and deploy RAG/CAG-...domain-accurate response generation. • Deploy and optimize conversational AI systems (internal & customer-facing chatbots) on AWS — leveraging SageMaker, ECS, Lambda, Bedrock, and API Gateway. • Integrate AI services seamlessly with existing backend systems and APIs for real-world interoperability. • Ensure scalability, observability, and cost efficiency across deployed AI workloads through best practices in MLOps and monitoring. • Collaborate with data and product teams to define model evaluation metrics, continuous imp...

...evaluation. The scope spans the full stack of an AI product. AI/NLP engineers will experiment with fine-tuning strategies and alignment techniques; language data engineers will own collection, cleaning and augmentation of text and speech corpora; backend and API developers will expose model capabilities securely; mobile and web developers will craft lightweight, accessible clients; and DevOps/MLOps engineers will automate training, versioning and scalable deployment to keep costs predictable. Core deliverables I expect: 1. A pre-trained foundation model with culturally aligned checkpoints and evaluation reports. 2. A multilingual dataset repository with documented provenance and licensing. 3. REST and gRPC APIs serving both text and speech endpoints, protected with OAuth2...

...building a pool of Telugu-speaking technology tutors who can comfortably teach online and take learners all the way from their very first “Hello World” through to advanced, production-grade projects. Right now the priority is Artificial Intelligence, Machine Learning, and Data Science, so you’ll be guiding students through everything from Python basics and Jupyter workflows to model deployment and MLOps best practices. Because our audience spans beginner, intermediate, and advanced levels, I’ll rely on you to tailor each session’s depth—introducing concepts gently for newcomers while still offering rigorous math, hands-on coding, and industry case studies for seasoned professionals. All classes will be delivered live over Zoom or a comparabl...

I’m ready to stand up a fully self-hosted language model that is fast, secure, and production-ready. Your job is to spin up a compact model with vLLM or , wrap it in a clean CUDA/Docker stack, and layer in the essentials—streaming responses, smart caching, guardrails, and real-ti...documentation. I’ll start with a paid test: deploy any quantized 7-13B model, enable streaming, then share an infra diagram and benchmark numbers (latency, memory footprint). Nail that and we’ll move on to caching, guardrails, and deeper evaluation. Show me links to prior self-hosting work and a quick sketch of your proposed setup. If you thrive in low-level inference, CUDA kernels, and containerized MLOps, let’s talk. price to be decided - please propose - fixed (1k to 1....

Looking for a python developer to work along my project team. If you match this requirment, connect with me. Job Title: Backend Engineer About the Role: We’re looking for a Backend / Infrastructure Engineer t...abstractions ● Love building tools that empower other engineers, analysts & scientists ● Have shipped production systems at scale with uptime and performance goals ● Thrive in hybrid roles where backend, infra, and data intersect ● Care deeply about data quality, lineage, and performance Bonus Points: ● Experience with Lakehouse architectures (e.g., Delta Lake, Apache Iceberg) ● Familiarity with MLOps workflows (MLflow, Feature Stores) ● Contributions to open-source projects ● Knowledge of event-driven architectures and CDC (Change Data Capture) patt...

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...product vision. Architect AI-powered features such as predictive analytics, intelligent HR insights, chatbots, anomaly detection, and workflow automation. Design scalable and modular architectures integrating ERP modules, APIs, data pipelines, and microservices. Mentor and upskill a team of AI, ERP, and full-stack engineers to accelerate delivery. Establish best practices for AI model deployment, MLOps, and performance optimization. Collaborate with business stakeholders to translate requirements into technical blueprints. Review architecture diagrams, code, and integration flows for quality and consistency. Evaluate and select AI frameworks, tools, and libraries best suited for ERP context. Guide integration with SAP, AWS, Azure, and other enterprise ecosystems. Ensure ...

...risk metrics such as VaR, drawdown, and Sharpe. Wherever possible, code needs to be modular so future data sources or model variations can slot in with minimal effort. Because the scope centers on the Indian context, corporate actions (splits, bonuses, dividends), F&O calendars, and local market holidays must be handled gracefully. I’m comfortable with Python, TensorFlow or PyTorch, and common MLOps stacks like Docker + CI/CD on AWS or GCP—use whichever combination you feel gets to a reliable, testable solution fastest. Deliverables • Clean, well-documented source code and notebooks • Automated pipelines for data ingestion and retraining • Trained models for both short- and long-term horizons • API / dashboard with prediction & ri...

I need an experienced technical writer to craft an in-depth blog post that dives straight into DevOps from an advanced perspective. The article must go beyond introductory explanations and offer actionable insight on automation tools, proven best practices, and real-world case studies. You’ll weave in your understanding of related domains—CI/CD pipelines, MLOps, AIOps, and security-first thinking—so the narrative shows how modern DevOps intersects with these areas rather than treating them as isolated topics. While DevOps is the main focus, brief yet meaningful references to tools such as Jenkins, GitLab CI, Kubernetes, Terraform, or Ansible will help ground your points. Deliverables • One well-structured, technically accurate blog article (roughly 1,500...

...C/C++, Arduino, Raspberry Pi, MQTT, RTOS IoT Cloud Platforms (AWS IoT, Azure IoT, Google IoT Core) Game Development Unity3D, Unreal Engine, C#, C++ AR/VR, 3D modeling, Metaverse environments AI, ML & Data Science Machine Learning, Deep Learning, Computer Vision Generative AI (LLMs, Transformers, LangChain, RAG) NLP, Speech Recognition, Chatbot Development TensorFlow, PyTorch, OpenAI, Hugging Face MLOps, Feature Stores, Model Deployment Predictive Analytics, Data Mining QA & Testing Manual Testing, Automation (Selenium, Cypress, Playwright) API Testing (Postman, SoapUI), Performance (JMeter, LoadRunner) Mobile App Testing, Security Testing Test Planning, Regression, CI/CD Integration Database Developers SQL (MySQL, PostgreSQL, Oracle, MS SQL Server) NoSQL (MongoDB, Cas...

...natural-language processing, and traditional machine-learning techniques to power a new deep-tech product that will move from prototype to production. I need end-to-end technical leadership that covers strategy, experimentation, and hands-on model delivery. Current status • Early-stage proof-of-concept code bases exist for both vision and text tasks, but they live in silos. • No unified data pipeline, no MLOps layer, and no clear roadmap tying research spikes to shippable features. Scope of work • Audit the existing repos, datasets, and annotation processes, then define a clean, version-controlled baseline. • Design an integrated architecture that supports multi-modal learning (images + text). • Select or fine-tune state-of-the-art models&mdas...

...engaging SCORM-compliant package that can drop straight into an LMS. You will craft the full learning experience—structured modules, multimedia explanations, coding walk-throughs, interactive notebooks, knowledge checks, and a summative capstone project—so that seasoned data professionals feel both challenged and supported. Think expert-level coverage of model optimisation, hyper-parameter tuning, MLOps workflows, and ethical considerations, presented through Articulate 360, Captivate, Rise, or any comparable tool you work best with. What I need from you • A detailed course blueprint with clear learning objectives and assessment strategy • All instructional assets: slides, scripts, demo code, datasets, quizzes, and solution keys • Final packag...

Need a working, end-to-end MLOps example that shows how a classification model is taken from code to a live, consumable service on AWS. The spotlight is firmly on Model deployment, but I still want the repository organised so the other pillars of MLOps (source control, automated testing, CI/CD, reproducible environments, basic monitoring hooks) are visible and ready to be extended. Project outline • Train at least two variants of an SGD-based classifier locally (Sklearn is fine) and version them. • Containerise the chosen model, push the image to ECR, and deploy it behind an AWS service that can expose a REST endpoint (SageMaker, ECS or EKS—use what makes the most sense). • Script all cloud resources with IaC (Terraform or CloudFormation). •...