Senior AI/ML Engineer (Traditional ML + Generative AI)

@neerajdhameliya

Hello, To improve your RAG system, I would use a multi-agent setup with LangGraph and a hybrid search approach (dense + sparse) using Qdrant or Pinecone. This helps reduce hallucinations, improves response accuracy, and lowers latency. It also saves token costs by sending simple structured queries to an XGBoost model, while more complex document-based queries are handled by the LLM agent workflow. I have 8+ years of freelance experience building production-ready AI solutions, combining traditional ML and Generative AI. I develop scalable FastAPI services, deploy with Docker, and manage ML pipelines using MLflow. I'm available to start immediately and would love to discuss your project. Best, Niral

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@corpmember29

Hi, I have 10+ years of experience building production-grade AI/ML solutions, combining classical Machine Learning with Generative AI to deliver scalable, real-world applications. My expertise includes Python, FastAPI, Scikit-learn, PyTorch, LangChain, LangGraph, OpenAI, Claude, RAG pipelines, vector databases (Pinecone/FAISS/Qdrant), PostgreSQL, Docker, Kubernetes, MLflow, and AWS. I've built intelligent chatbots, multi-agent systems, semantic search, AI automation platforms, and predictive ML models, handling the complete lifecycle from data engineering and model development to deployment, monitoring, and optimization. I also have hands-on experience with CI/CD, MLOps, prompt engineering, tool calling, structured outputs, and scalable REST APIs. I write clean, maintainable, production-ready code, collaborate effectively across teams, and enjoy taking ownership of AI projects from research through deployment. I'd be excited to contribute to your team and help build robust, enterprise-grade AI products.

@PINESCRIPTERR

Hi, I can support this role across both production ML and modern Generative AI systems. My experience covers Python-based ML pipelines, structured-data modelling, feature engineering, evaluation, FastAPI services, RAG workflows, LLM integrations, vector databases, prompt/tool design, and deployment-ready architecture. For classical ML, I’m comfortable with Pandas, NumPy, Scikit-learn, XGBoost/LightGBM-style workflows, SQL-heavy datasets, model validation, experimentation, and monitoring. For GenAI, I can build RAG systems, agent workflows, structured outputs, tool/function calling, memory patterns, retrieval evaluation, reranking, and API integrations with OpenAI/Claude/Gemini or open-source models. My preferred production stack would be Python, FastAPI, PostgreSQL, Redis, Qdrant/Pinecone/Weaviate, LangGraph or LangChain, Docker, GitHub Actions, MLflow, and AWS/GCP/Azure depending on your infrastructure. I focus on reliable architecture: clean services, evaluation loops, logging, cost/latency controls, CI/CD, and clear documentation rather than fragile demos. I can also help with model versioning, experiment tracking, deployment, monitoring, and improving existing AI systems from prototype to production quality. Question 1: Is the first priority classical ML, RAG, multi-agent workflows, or production/MLOps cleanup? Question 2: Are you already using a cloud/vector database stack, or should I recommend one based on the product requirements? Regards, Houssame

@juanpablomacf

I can support you as a Senior AI/ML Engineer, combining classical ML expertise with production‑grade Generative AI systems. I’ve built end‑to‑end pipelines, multi‑agent architectures, RAG systems and scalable MLOps deployments. If helpful, I can explain how I design a RAG pipeline or how I structure a multi‑agent system for reliable orchestration. My approach: • Predictive models with scikit‑learn, XGBoost/LightGBM, feature engineering and evaluation. • LLM apps using LangGraph/LangChain, structured outputs, tool calling and memory. • RAG with Pinecone/Qdrant, hybrid retrieval and reranking. • FastAPI services, Docker/Kubernetes, CI/CD, MLflow, monitoring and versioning. • Automated evaluation pipelines for LLMs, agents and RAG accuracy/latency. • Clean, production‑ready Python code with strong SQL for large datasets. I’ve delivered ML + GenAI systems for enterprise products, owning the full lifecycle from data prep to deployment. Ready to work long‑term and deliver consistently.

@mykhaylob4

✋ Hi there. I can build production-ready traditional ML and Generative AI systems, including multi-agent architectures, RAG pipelines, and scalable ML services. ✔️ I have strong experience across the full AI lifecycle, from predictive modeling and feature engineering to building LLM applications with LangGraph, RAG using vector databases, and deploying APIs with FastAPI and Docker. I will develop this by designing end-to-end ML solutions, building evaluation pipelines, and implementing MLOps practices for monitoring and continuous improvement. Please click the 'Chat' button to start our valuable conversation. Looking forward to collaborating with you! Best regards, Mykhaylo

@eduard141

Dear Hiring Manager, I am very interested in your AI/ML Engineer position. With strong experience in both traditional Machine Learning and modern Generative AI, I have built and deployed production-ready AI solutions across the entire development lifecycle. My expertise includes: • Python, Pandas, NumPy, Scikit-learn, XGBoost, and SQL for predictive modeling and data analysis. • LLM applications using OpenAI, Claude, Gemini, LangChain, and LangGraph. • Multi-agent systems, RAG pipelines, prompt engineering, tool calling, and memory architectures. • Vector databases including Pinecone, Qdrant, FAISS, and Chroma with hybrid retrieval and reranking. • Production deployment using FastAPI, Docker, Kubernetes, CI/CD, MLflow, and cloud platforms such as AWS and GCP. I have experience developing scalable AI applications, APIs, and intelligent automation systems with a strong focus on performance, maintainability, and real-world business impact. I am comfortable owning projects from research and architecture design through deployment and continuous improvement. I would appreciate the opportunity to discuss your requirements and demonstrate how my experience can contribute to your team and help build innovative AI products. Thank you for your time and consideration. I look forward to speaking with you. Best regards, Eduard

@PrateekP3003

Hi There! In your project description, you are looking for an AI/ML Engineer who can build production-ready machine learning solutions, LLM applications, multi-agent systems, RAG pipelines, and scalable AI services using modern MLOps practices. Right? I AM GLAD TO TELL YOU THAT I HAVE ALREADY WORKED ON SIMILAR AI & MACHINE LEARNING PROJECTS. With my knowledge and skills, I am ready to help you build and deploy production-grade AI systems with the required features and capabilities: End-to-End ML Pipeline Development LLM Applications (OpenAI, Claude, Gemini) Multi-Agent Systems (LangGraph/CrewAI) RAG & Vector Database Integration Prompt Engineering & Tool Calling FastAPI & Scalable REST APIs MLflow, Docker & Kubernetes AWS/GCP/Azure Deployment Model Monitoring & CI/CD Performance Optimization & Documentation I have 5+ years of experience developing AI, ML, and data-driven applications using Python, FastAPI, Scikit-learn, PyTorch, LangChain, LangGraph, PostgreSQL, Redis, Docker, and cloud platforms. I specialize in building scalable, production-ready AI solutions with clean architecture, maintainable code, and reliable deployment pipelines. EVERYTHING AS YOU ARE PLANNING, I WILL EXECUTE. Please get in touch with me via chat so we can continue discussing the project. Thanks & Regards Prateek

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience in software development, with a background in AI platform and machine learning application development. I am familiar with Python, Machine Learning, Data Science, LangChain, LangGraph, AI Agents, Large Language Models (LLMs), RAG, Vector Databases, FastAPI, MLOps, Docker, Kubernetes, TypeScript, and AI Model Development. For this project, I can build production-ready ML and LLM solutions by combining traditional machine learning with scalable RAG pipelines, multi-agent workflows, FastAPI services, and MLOps practices, while optimizing retrieval quality, latency, and model performance for reliable deployment in production environments. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@carlosd178

Hello, As a result of reviewing your project requirements, I understand that you need an AI/ML Engineer who can handle both traditional machine learning and modern GenAI systems from data preparation to production deployment. I have experience handling similar AI and machine learning projects and I’m available to start right now. I bring strong expertise in Python, Machine Learning, Data Science, LLMs, LangChain, LangGraph, AI Agents, RAG, FastAPI, TypeScript, MLOps, Docker, Kubernetes, MLflow, SQL, vector databases, and cloud deployment. One key challenge in projects like this is building AI systems that are not just prototypes, but reliable, scalable, measurable, and maintainable in production. My simple approach would be to first understand the product goal and data flow, then design the ML/LLM architecture, build RAG or agent workflows where needed, add evaluation and monitoring, and deploy through clean APIs with proper versioning and CI/CD. I have a couple of quick questions. • Are you currently building a new AI platform from scratch or improving an existing system? • Which cloud and vector database do you prefer for deployment? I would be glad to discuss further details and am ready to start immediately. Looking forward to hearing from you. Best regards, Carlos.

@Eliteinno

Hey, the focus on multi-agent systems with explicit planning and orchestration really caught my eye. I've been building these using LangGraph for complex workflows, especially where agents need to dynamically choose tools and manage their own state. The real challenge often comes with robust memory management and ensuring agents don't hallucinate when chaining actions, which I tackle by integrating structured output validation and clear context windows. Are you leaning towards a specific framework like CrewAI or AutoGen for the agent orchestration?

@exequiel12ifran

Hello, I have just read your job description carefully. I have experience building production AI systems with Python, FastAPI, Scikit-learn, LangChain, LangGraph, OpenAI, Claude, vector databases, PostgreSQL, Redis, Docker, and cloud deployments. I have worked on classical ML models, RAG pipelines, multi-agent workflows, prompt optimization, tool calling, evaluation pipelines, and scalable AI APIs. I am comfortable owning projects from data preparation through deployment, monitoring, and improvement. I have one question for you. Are you mainly hiring for a specific AI product already in production, or for building the platform architecture from the ground up? Thanks for considering my proposal, Lautaro

@IbrahimAltaf

I have hands-on experience building LLM-powered chatbots and AI automation systems, working directly with large language models to design conversational flows, prompt logic, and tool integrations for real business use cases. My background is full-stack engineering, which means I bring solid API design, backend architecture, and production deployment skills alongside the AI layer, not just isolated scripts. I understand this role spans classical ML, production RAG systems, MLOps, and multi-agent orchestration at a senior level. My strongest fit is the Generative AI side: LLM integration, chatbot design, and automation workflows, and I would be upfront that my classical ML and MLOps depth is still growing rather than at the 5+ year production level this role describes. If you are open to bringing someone in specifically for the LLM and automation portion of this work, or pairing with your existing ML team, I would be glad to contribute there and grow into the broader scope over time. Happy to share examples of chatbot and automation work I have delivered.

@rbtech1984

Hi There!!! ★★★★ (I can build production-ready ML, RAG, and AI agent systems with scalable MLOps and LLM integration.) ★★★★ I carefully read your requirements and understand you're looking for an AI/ML Engineer experienced in both traditional Machine Learning and modern Generative AI. From end-to-end ML pipelines to RAG, multi-agent systems, deployment, and monitoring, I can contribute across the full AI lifecycle. ⚜ End-to-end ML development ⚜ RAG & AI agent systems ⚜ LangChain/LangGraph integration ⚜ FastAPI & scalable AI APIs ⚜ Vector database implementation ⚜ MLOps, Docker & Kubernetes ⚜ LLM evaluation & optimization I've worked on AI solutions involving Python, FastAPI, LangChain, OpenAI, vector databases, and production ML workflows. I enjoy solving complex AI problems while writing clean, maintainable code. I'll follow a structured development process, optimize performance and cost, and ensure the solution is production ready. I'd be glad to discuss your goals and see how I can contribute to your team. Warm Regards, Farhin B.

@nicolasalexandro

I am confident in delivering robust AI and ML solutions tailored to your project’s needs, including both traditional machine learning and generative AI techniques. I understand you need an expert who can integrate these advanced methods effectively. With years of experience in developing and deploying AI models across diverse industries, I have a proven track record in improving model performance and scalability. My expertise covers the entire ML pipeline from data preprocessing to model tuning and evaluation. I approach projects with clarity and structured workflows, ensuring smooth collaboration and timely delivery. Happy to review your requirements in detail and discuss how we can move forward.

@dushyant317

With 5+ years of AI/ML engineering experience, I specialize in building production-grade machine learning and Generative AI solutions, including RAG pipelines, multi-agent systems, LangGraph/LangChain workflows, FastAPI services, vector databases, MLOps, and scalable cloud deployments on AWS/GCP/Azure. I can contribute across the complete AI lifecycle—from data engineering and model development to deployment, monitoring, and continuous optimization—while delivering clean, maintainable, production-ready code.

@krupnovdenis

Hello! We can cover these tasks through an external contractor format. 1. Are you open to working with an external contractor or team for these tasks? 2. Which tasks and technologies need to be covered first? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@iterativeTech

With over 5 years of experience in building production-ready ML systems and a deep understanding of Knowledge Graphs and GraphRAG, I'm confident in my ability to take on the role of Senior AI/ML Engineer for your project. Having worked extensively with generative AI, including LangChain, and with fine-tuning LLMs such as LoRA/QLoRA/PEFT, I am well-versed in implementing retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone and FAISS. As an AI-driven Cloud Data Engineer, my skills aren't limited to just ML - I am also proficient in Kubernetes, MLflow, Docker; making me a perfect fit for MLOps and production engineering tasks. My experience goes beyond theoretical knowledge - I have successfully deployed many production-grade LLM applications and have optimized prompts/retrieval quality for improved system performance. What sets me apart is not only my technical expertise but also my focus on achieving tangible ROI through intelligent systems - something that aligns well with your vision. Today's businesses need solutions that are not just technically viable, but also financially beneficial. With my strong business-first mindset framing every technical decision, I ensure that the solutions I build are not just scalable, cost-efficient but also aligned with long-term goals and challenges. Choosing me will mean choosing a partner who understands your needs completely and can deliver result-oriented solutions at every stage of the AI lifecycle.