AI-Powered Cybersecurity Platform

@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

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@vishal1145

Your RAG pipeline will collapse under production load if the vector store cannot handle concurrent queries while your LLM inference runs. Most teams discover this bottleneck after launch when latency spikes to 8+ seconds during peak traffic. Quick questions - are you planning to host embeddings and inference on separate SageMaker endpoints to isolate scaling? And what's your target p95 latency for end-to-end retrieval plus generation? Here is the architectural approach: - VECTOR DATABASES: Deploy Pinecone or Weaviate with sharded indexing so retrieval stays under 200ms even at 1000 QPS, then cache frequent queries in Redis. - AWS SAGEMAKER: Fine-tune your LLM using LoRA adapters on SageMaker Training Jobs, then deploy multi-model endpoints with auto-scaling to keep inference cost predictable. - AGENTIC AI: Build the agent loop using LangChain's ReAct framework with tool-calling validation so the system can retry failed actions without human intervention. I've built three production RAG systems for enterprise clients that now serve 50K+ daily users with sub-2s response times. Let's schedule a 20-minute architecture review so I can map your data sources and propose the exact SageMaker configuration before you commit budget.

@rakhshanz3

With my strong background in AI and software engineering, I bring a wealth of experience and knowledge to the table. Having worked on numerous AI-driven solutions, including developing generative AI applications and designing NLP systems, I'm confident that your project can benefit from my expertise. From data science to APILastly, I highly value a quick turnaround without compromising quality which reflects in my capability to deliver one-click deployments via CI/CD or IaC. My clean code philosophy and documentation practices ensure shared Git repos are always neat and organized—I'm confident that these project management skills will simplify our collaboration while maintaining utmost professionalism. Let's team up to build your agentic, retrieval-augmented generative AI infrastructure—a solution far beyond simple prompt engineering!

@ashinnd84

Hi there, we are a team of AI ML automation, Full Stack Web and Mobile App developers and we can do this project in no time. Thanks Ashish Kumar.

@mahads31

As the founder of Solves Inn, I have spearheaded numerous challenging projects like the one you've outlined. Our focus on building efficient and reliable systems aligns perfectly with your need to create agentic AI architecture. We possess the skills and expertise in the technologies you already use - Python, Lambda, PyTorch, AWS EKS and SageMaker - making our entry into your project seamless. Our experience in training large language models for both public and private datasets has equipped us with a deep understanding of prompt engineering and model optimisation, which I believe will be invaluable to your project. We understand that smooth integration is paramount, hence we don't just deliver quality models but also make sure they are seamlessly woven into existing services. In addition to technical excellence, I assure you a strong commitment to documentation, reproducibility, and automated testing for easy and optimized deployments. With our proven track record in delivering scalable AI systems from end-to-end, we can certainly help your business empower its problem-solving capacities by efficiently producing relevant content at low latency. Let's collaborate and get this innovative initiative live quickly!

@dushyant317

I can design and deliver a production-grade Agentic AI cybersecurity platform using LLMs, RAG, autonomous agents, and secure AI services tailored for SOC operations, threat intelligence, and incident response. With expertise in Python, LangGraph/CrewAI, LangChain, LlamaIndex, Hugging Face, PyTorch, vector databases (Pinecone/Milvus/FAISS), AWS, Docker, Kubernetes, and CI/CD, I can build scalable data pipelines, fine-tuned cybersecurity models, multi-step AI agents, and low-latency REST/gRPC APIs with enterprise-grade monitoring, security, and deployment automation.

@srdank001

Hi. I can help build a production-ready agentic AI platform, from data ingestion and RAG pipelines to model serving and integration with your existing services. I'd use Python, LangChain/LangGraph, PyTorch, vector databases, and AWS EKS/SageMaker, with reproducible CI/CD and well-defined REST or gRPC APIs. Before choosing models, I'd benchmark your data and latency targets to avoid unnecessary fine-tuning costs. Are your private datasets primarily documents, structured data, or both? Thanks Srdan.

@coretus

Hi, I specialise in AI app development and can help you build the full in-house agentic RAG stack in Python, from ingestion to deployment. What I can deliver: 1. Scalable data pipeline and vector store setup for RAG. 2. LLM fine-tuning or optimisation for your private data, with latency and cost in mind. 3. Agent workflow for multi-step tool use and decision making. 4. REST API layer, example clients, and clean code in shared repo. 5. Deployment scripts for AWS EKS or SageMaker, with tests and documentation. My approach will be practical. First I will map the data flow, model choice, and target benchmarks. Then I will build the pipeline, integrate the agent layer, and validate accuracy and latency on your test set. Since you already use Python, LangChain, PyTorch, and AWS, integration should be smooth. I am pricing this at the top of your budget because the scope includes model work, infrastructure, and production integration, not just prompt work. If helpful, I can start with a short architecture plan and milestone breakdown. Do you already have the domain dataset and benchmark set ready? Best Anil Kamani

@tyronet2

You need an AI cybersecurity platform that combines LLMs, RAG pipelines, and autonomous agents for threat analysis and security operations. I built production AI workflows at Marin Software using Python, AWS Lambda, LangChain-based agents, and real-time data pipelines. My experience includes designing LLM integrations, retrieval workflows, and backend services that connect AI capabilities with existing applications. For this platform, I can build the AI service layer with Python, LangChain/LlamaIndex-style RAG architecture, vector database integration, and secure REST APIs. I can help structure agent workflows for reasoning, tool execution, and cybersecurity knowledge retrieval while preparing Docker-based deployment and CI/CD integration. I’d like to review your current security data sources, preferred LLM provider, and deployment environment before defining the architecture.

@iterativeTech

As a seasoned AI professional with a focus on driving Return on Investment (ROI) for my clients, I possess the skills and experience crucial to developing your AI-powered cybersecurity platform. Having worked across industries such as finance, healthcare, and enterprise, I fully comprehend the complex demands of your project and understand the importance of delivering accurate, low-latency responses along with seamless integration. My proficiency in Python, AI/ML development, Agentic AI frameworks, and building Generative AI solutions makes me uniquely positioned to create the sophisticated autonomous AI agents your project calls for. Data engineering, model development to deployment, optimization, and production integration—all of these fall under my domain of expertise. I have extensive experience with fine-tuning LLMs using proprietary datasets and placing security at the forefront while designing secure deployment pipelines and model lifecycle management. With my strong background in cloud data engineering and architecture alongside real-time analytics using Power BI and Tableau, I bring robustness and efficiency to all aspects of the project- precisely what you require.

@ZohaibSathio

I've shipped exactly this stack in production, not as a prototype. Closest proof: a private document-search API over 100K+ documents - FastAPI + Pinecone, top-k with re-ranking and source attribution, 95% top-3 relevance at sub-500ms. That is your ingestion -> vector-store -> RAG path end to end. On the agentic side: an autonomous AI board member that attends regulatory meetings, runs RAG over regulation, reasons, and votes aloud - 200+ motions at 97% alignment with the human vote. Also a multilingual voice-banking platform serving 500K+ commands/month at 99.3% uptime, sub-800ms. How I'd approach yours: - Ingestion, chunking and embeddings into pgvector/Pinecone, with re-indexing that doesn't take the service down - RAG with re-ranking and citations, so answers are auditable instead of confidently wrong - Fine-tune only where retrieval provably plateaus - it is usually cheaper to fix retrieval than to train - Clean REST/gRPC contracts + Docker so your back-end and front-end teams consume it without friction The failure mode I'd design against first: a RAG system that demos beautifully, then drifts once real private data lands. I gate that with an eval set and a confidence threshold that routes low-confidence answers to human review instead of guessing. What does the private dataset look like - roughly how many documents, and what latency budget are your existing services holding you to?

@jagratip3

Hi, I’m Jagrati, and I’m ready to help build your production-grade agentic RAG + LLM platform. I have experience with AI/ML development, LLM integrations, RAG pipelines, vector databases, Python, LangChain, and scalable cloud-based deployments. I understand your goal is to move beyond basic prompting and create intelligent systems capable of retrieval, reasoning, tool usage, and autonomous workflows. I can help design the complete AI stack, from data ingestion and embeddings to model optimization and backend integration. I can deliver: • Scalable RAG pipelines with vector stores • LLM fine-tuning and inference optimization • Agent workflows with multi-step tool execution • REST/gRPC integration layers • Clean Git-based code, deployment scripts, and documentation • Benchmarking for latency, accuracy, and reliability I’m comfortable working with Python, PyTorch, LangChain, AWS, and production AI architectures. I’m ready to discuss your data sources, infrastructure, and deployment goals to get started. Best regards, Jagrati

@MuhammadAkif8

I understand that your project is focused on developing advanced AI capabilities within your organization. I have extensive experience in the field of AI and automation, making me uniquely positioned to contribute to your initiative. My proficiency in Python aligns perfectly with your existing tech stack, and my previous work with OpenAI has given me a deep understanding of generative AI models, which I can leverage in building your RAG + LLM system. My strong data engineering background makes me adept at building scalable data pipelines, which will be crucial in the successful implementation of a RAG workflow. Furthermore, my expertise in training and fine-tuning large language models for private datasets will ensure that I can provide you with highly accurate and efficient models that meet your defined criteria for accuracy and latency. In addition to my technical skills, I take pride in my ability to deliver solutions quickly without compromising on quality. This means you can trust that I'll provide you with a clean, documented codebase complete with deployment scripts for easy integration into your existing infrastructure. Not only will my work be reproducible, but it will also demonstrate one-click deployability, ensuring minimal friction during testing and production phases. Let me bring my automation expertise to help transform your organization into one that operates with cutting-edge AI technologies. AKif F

@tatumm17

I can help you design and deliver a production-grade, agentic RAG system that fits cleanly into your existing Python, LangChain, PyTorch, and AWS (EKS/SageMaker) stack, with strict latency, accuracy, and integration requirements in mind. I’ve led end-to-end builds of RAG and tool-using agents: data ingestion to vector stores, private-finetuned models, benchmarking, and deployment with IaC and CI/CD, exposing REST/gRPC services that backend and frontend teams can adopt easily. My approach would be to first align on metrics and workflows, then implement the ingestion and training pipelines, agent framework, and deployment scripts in a shared repo, ending with staging clients to validate end-to-end behaviour. I would love to chat more about your project! Regards

@heenafullstacken

For Build Agentic AI In-House, I can turn the source-to-destination requirement into a dependable pipeline that is easy to operate after delivery. The build can include ingestion, transformations, schema checks, incremental loading, duplicate protection, scheduling, alerts, retries, and clear run documentation. My first step would be a compact source/volume/frequency/SLA map, followed by the simplest architecture that meets the actual workload. What are the source systems, target warehouse, approximate daily volume, and acceptable processing delay? Data work: https://www.freelancer.in/u/heenafullstacken Regards, Heena A Plus IT House

@shanuka1223

Hi there, I am excited to help you build a production-ready, agentic AI solution inside your organization. I understand you are looking to move beyond simple prompt engineering to a fully autonomous RAG setup with low-latency capabilities. Here is how I will approach the key deliverables: 1. Data & Vector Pipeline: Setting up a robust, scalable data ingestion pipeline feeding into a high-performance vector store for domain-specific context retrieval. 2. Model Optimization: Fine-tuning and optimizing the LLMs using PyTorch and AWS SageMaker to ensure high accuracy while keeping inference fast and cost-effective. 3. Multi-Step Agent Framework: Implementing an autonomous agent using LangChain that can reason, handle multi-step tool calls, and execute decision-making tasks cleanly. 4. Clean Integration: Packaging the models with clear REST or gRPC endpoints so your frontend and backend teams can integrate seamlessly. I will also provide clear documentation and CI/CD deployment scripts. I have strong experience in Python, AI model development, and enterprise deployments on AWS. Let's connect to discuss your exact test benchmarks and get this project live quickly. Best regards, Sujani

@RobertBehrmannJr

You're building a full agentic RAG stack in-house — ingestion pipeline, vector store, fine-tuned LLM, multi-step agent framework, and REST endpoints that your existing teams can actually consume without hand-holding. That's a serious scope, and I've shipped exactly this kind of system: LangChain-based RAG pipelines with structured retrieval, OpenAI-compatible inference endpoints, and Docker/CI deployment scripts that make the whole thing reproducible. My approach: stand up the ingestion and vector store first (likely pgvector or Chroma, feeding from your existing data sources), layer the agent framework on top using LangChain's tool-calling abstractions wired to SageMaker endpoints, then expose everything via a clean FastAPI REST layer with example clients for your front-end and back-end teams. I'll keep the codebase modular so your team can swap model providers or vector backends without rebuilding from scratch. One question before I scope this precisely: do you already have a candidate dataset and defined accuracy/latency targets, or is establishing those baselines part of what you need me to help define in the first phase?

@BahadirAksar

Hi there, I've built exactly this kind of system — an agentic RAG platform called Şahsar that runs production-grade LLM workflows with vector stores, multi-agent orchestration, and clean API endpoints. It uses LangChain, FastAPI, and connects to vector databases (Qdrant) for retrieval-augmented generation. Here's how I'd approach your project: 1. Data Pipeline & Vector Store: Build a scalable ingestion pipeline that processes your private datasets, chunks them intelligently, and indexes them into a vector store (Qdrant/Pinecone/Weaviate — whichever fits your latency needs). 2. LLM Fine-Tuning: Fine-tune open-source models (Llama/Mistral) on your domain data using SageMaker or direct PyTorch training pipelines. Optimize for accuracy and inference speed. 3. Agent Framework: Implement a tool-calling agent that can reason across multiple steps, query the vector store, call REST APIs, and make decisions autonomously — exactly what Şahsar's sub-agent system does. 4. Integration: Wire everything into your existing AWS infrastructure with gRPC/REST endpoints, CI/CD deployment scripts, and clear documentation. My tech stack (Python, LangChain, FastAPI, PyTorch, AWS) aligns perfectly with yours. I deliver clean, documented, production-ready code. Let's get this live quickly!