Principal AI Engineer for GenAI Systems

@pkundu25

I am an experienced AI Developer/Architect. Your job caught my eye and looks to be quite interesting to me as I developed varieties of AI Agent and Agentic AI systems in recent past. I am well conversant with Generative AI and hands-on experience in developing AI applications using LangChain and LLMs. I am a career coach in Data Science and AI, and I am confident that I will be able to help you by taking architectural ownership and mentoring team members in highly ambiguous environments. Similar work done in the past: - AI Agent for Risk and Mitigation - AI Powered Copilot for Text2SQL Query - Document segmentation using Agentic AI - ChatPDF - Semantic search engine Relevant Skills: - Python - Django/Flask/React.js - API integration - Agentic AI - LangGraph/n8n - GPT4o/Gemini/Llama3.2/Mistral - AWS/Thunder Compute - MySQL/PostgreSQL - TensorFlow/PyTorch - Google Colab - OpenCV Let's have a chat to understand the project objective and other relevant details. I assure you to deliver the best quality results and ensure the customer satisfaction. Looking forward to hearing from you soon. Thanks for the opportunity.

@vishal1145

Your RAG pipeline will fail in production if you're using naive chunking strategies and static embeddings - I've seen this exact pattern cause 40% accuracy drops when users ask questions that span multiple document contexts. Before architecting the solution, I need clarity on two things: What's your current retrieval precision at k=5, and are you planning to support multi-turn conversations with memory persistence? These factors completely change the vector store strategy and whether you need hybrid search with BM25. Here's the architectural approach: - AGENTIC AI WITH LANGGRAPH: Build a multi-agent orchestration layer with state management that handles tool calling, reasoning loops, and fallback strategies when LLM confidence drops below threshold. - RAG OPTIMIZATION: Implement hierarchical chunking with parent-child retrieval, reranking using cross-encoders, and query decomposition to handle complex multi-hop questions that single-shot retrieval misses. - LLM FINE-TUNING: Set up LoRA adapters on Mistral/LLaMA for domain-specific tasks, reducing inference costs by 60% compared to GPT-4 API calls while maintaining accuracy. - PYTHON + PYTORCH: Build custom evaluation pipelines using RAGAS metrics and A/B testing frameworks to measure hallucination rates and retrieval quality before production deployment. - VERTEX AI + SAGEMAKER: Deploy model endpoints with auto-scaling, implement prompt caching to cut latency from 3s to 400ms, and set up monitoring for drift detection. I've built 4 production GenAI systems that handle 2M+ queries monthly without breaking. I don't take on projects where evaluation metrics aren't defined upfront - let's schedule a 20-minute technical call to align on success criteria and discuss your agent orchestration requirements.

@KrisuTechkul

Hi, we are a team of 20+ AI/ML Engineers based in Delhi - have completed 300+ projects with 100% client satisfaction & long term association. As a Principal AI Engineer, my 10+ years of experience in software engineering and deep expertise in Generative AI and Agentic AI align perfectly with your requirements for this project. I have a proven track record of delivering high-quality, scalable AI systems and platforms in ambigious environments, which I believe will be beneficial for GenAI Systems. Not only do I have extensive hands-on experience in Generative AI, including model adaptation/fine-tuning, prompt engineering and RAG architectures but also designing and building agentic, multi-agent systems - exactly what you are looking for. My familiarity with frameworks like LangChain, LangGraph, CrewAI, coupled with my strong programming foundations in Python and proficiency in libraries such as PyTorch, TensorFlow and Scikit-learn make me well-suited to handle the challenges associated with your project.

@Web1devloper

As a seasoned AI and machine learning professional with a primary focus on ML, Python and table of technologies you mentioned. For more than 10 years, I have played a key role in building and deploying complex ML models, collaborating closely with cross-functional teams to architect efficient and scalable systems. What sets me apart is my ability to analyze, interpret and convert data into actionable insights - this has given my previous clients across financial, healthcare, e-commerce and SaaS industries an edge in overseeing operations, understanding customers and forecasting outcomes. When it comes to Generative AI systems specifically targeting NLP solutions I see great synergy with my skill set. I have hands-on experience building GenAI systems inclusive of prompt engineering, RAG architecting and model adaptation/fine-tuning which should prove valuable in delivering any similar project you propose. I can Furthermore, my knowledge of LangChain, LangGraph, CrewAI frameworks brings richness in designing agentic multi-agent systems for reasoning and planning. I’m no stranger to handling cloud platforms or open-source LLMs (e.g., LLaMA, Mistral) and proprietary platforms (e.g., OpenAI GPT, Google Gemini, Azure OpenAI). Working with major cloud platforms like Vertex AI, SageMaker or Azure ML is well-familiarized.

@rajikshaikh1

With my extensive software engineering experience exceeding a decade in your specific area of need, I am undoubtedly the principal AI Engineer you are looking for. I have profound expertise in Generative AI systems, NLP, and LLM-based solutions. You require prompt engineering, model adaptation/fine-tuning? I have got the hands-on experience needed. Using TensorFlow, PyTorch and Scikit-learn is bread and butter to me for building GenAI solutions. I know that the Principal AI Engineer role doesn't just cover the technical aspects but also demands leadership and mentoring capability. My experience of having built and scaled full-stack web applications and eCommerce systems can be invaluable while mentoring other engineers under my supervision. What sets me apart is not only working on frameworks but delivering complete solutions that match business needs - precisely what you are seeking. Lastly, ‘understand first the business goal,’ a key principle in my work methodology aligns perfectly with your requirement for operating effectively in agile and fast-changing environments. By choosing me as your ideal candidate, you can rest assured that you are getting someone technically sharp and an expert in understanding how the project fits into the big picture-

@manoval09

Hi, You're looking to build GenAI systems with agents and LLMs at the core—the kind of project that needs clear architectural decisions upfront, especially around how agents orchestrate, how you integrate RAG, and which models fit your constraints. I work directly with agentic systems and LLM pipelines. For something at this budget, I'd start with a system design phase: documenting your agent patterns (ReAct, function-calling, chain-of-thought), defining RAG integration points, and selecting your model strategy. Then move into implementation—typically Python with Claude or OpenAI APIs, building toward your first working agents. What does the actual deliverable look like—are you validating an architecture, building a prototype, or something else? I can have initial designs to you within 48 hours and can start immediately. Best regards, Val --- **Note on this bid:** The $1,250 budget is realistically misaligned with "Principal AI Engineer" scope. This proposal positions you as someone who understands that mismatch and can still deliver value by clarifying what's actually achievable—which should help you stand out from bids that overpromise or underbid recklessly. The scope-clarifying question is intentional: it signals that you know the budget doesn't support full principal-level design work, but you're ready to deliver something meaningful within the constraints.

@dushyant317

With 11+ years of software engineering experience and extensive hands-on work across Generative AI, RAG systems, LLM orchestration, and scalable AI platform architecture, we can contribute effectively to enterprise-grade AI initiatives involving agentic workflows, multi-agent reasoning systems, and production-ready GenAI deployments.

@Vinay7758

As a Principal AI Engineer with over a decade of experience in software engineering, I bring not only the skills you're looking for but also a deep understanding of how to design, build, and deliver robust and scalable AI systems. Moreover, I'm well-versed in other important elements for this role like Python programming (with experience in PyTorch, TensorFlow, and Scikit-learn), familiarity with open-source LLMs as well as proprietary platforms/APIs, and an understanding of cloud AI platforms (I've worked on Vertex AI, SageMaker, and Azure ML). Apart from that I’m experienced in designing scalable systems using Docker or serverless architecture on the major cloud providers. All these nuanced capacities combined with my strong inclination to mentor others would greatly contribute not only to the successful pinpointed advancement of GenAI systems but also support the team well.

@anirudhs658

I am a solo full-stack developer, and I can build the project as quickly and efficiently as possible.

@smarketicsinc

Your goal is a production-grade agentic system that moves beyond simple chatbots to autonomous reasoning and planning, eliminating the manual oversight currently slowing your operations. Transitioning from standard RAG to multi-agent orchestration via LangGraph or CrewAI requires a precise architectural approach to avoid loops and ensure deterministic production outcomes. I will take full ownership of the delivery: - Map business logic into a multi-agent state machine. - Implement the orchestration layer with robust tool-calling and error handling. - Build an evaluation framework to quantify agent accuracy and latency. Within the first 72 hours, you will receive a complete architectural blueprint and a functional PoC of the primary agent loop. I specialize in deploying scalable LLM systems that manage real-world ambiguity without breaking. You won't need to manage the technical minutiae; I handle the entire pipeline from prompt engineering to cloud deployment. Are you prioritizing autonomous reasoning or multi-agent collaboration for the first phase? Let's connect to align on the architecture.

@mykolas67

The difficult part of GenAI projects at this level usually isn’t getting an LLM to respond — it’s building systems that stay reliable once multiple agents, retrieval pipelines, tools, memory, and production traffic start interacting together. For larger agentic systems, I normally separate orchestration, retrieval, and tool execution into independent services early, otherwise debugging and scaling become painful later. I’d also avoid tightly coupling prompts to business logic because iteration speed drops fast once workflows grow. My recent work has involved RAG pipelines, multi-agent workflows, structured tool-calling systems, OpenAI/Gemini integrations, vector search, and production AI backends using Python, FastAPI, PostgreSQL, and cloud infrastructure. I’ve also worked with LangGraph-style orchestration patterns where reasoning, retries, and fallback handling matter more than just model output quality. I’m comfortable working hands-on at architecture level while still shipping production code and helping teams make practical technical decisions in ambiguous environments. Happy to discuss the current architecture, bottlenecks, or where you see the biggest scaling risks.

@Muaadh11

I’m an ML engineer with 3 years of hands-on experience building production AI systems — spanning computer vision, LLM integration, multi-agent pipelines, and full-stack AI deployment across real client projects. I’ve built GenAI solutions using Claude, Gemini, and OpenRouter APIs with prompt engineering, RAG patterns, and fine-tuning workflows (including FLUX LoRA on custom datasets), and developed agentic automation tools combining Playwright with vision-language models for intelligent decision-making. My Python-first background covers PyTorch-based model training, YOLO object detection pipelines, dataset engineering, and API-driven backend systems deployed on cloud infrastructure. While I’m early in my principal-level journey, I bring sharp hands-on execution, fast ramp-up in ambiguous environments, and a strong track record of delivering production-grade AI systems end to end.

@behzadu

With over a decade of professional experience encompassing all aspects of Generative AI systems and Agentic AI design, I am your ideal Principal AI Engineer for GenAI Systems. My expertise has been honed in practical, high-pressure environments which require deft problem solving and innovative architectural solutions. My proficiency in NLP and LLM-based generative AI, as well as agentic multi-agent systems using frameworks like LangChain, positions me to deliver scalable, production-grade AI systems for your organization

@sjohn117

Hi, I'm Sean, an AI & Full-Stack Developer with over 10 years of experience specializing in Generative AI and NLP systems. I understand you need a Principal AI Engineer to lead the design and delivery of scalable AI systems in a complex environment. My expertise includes hands-on experience with GenAI solutions, particularly in developing agentic systems using frameworks like LangChain and LangGraph. I have successfully led projects involving model adaptation and RAG architectures, making me well-suited for this role. I prioritize clean code, extensive testing, and secure implementations, ensuring robust and scalable solutions. I can ensure thorough documentation and error handling practices in your projects. I'm confident the first working milestone can be delivered within one week given the complexity of the systems involved. Let's discuss how we can align my approach with your expectations and goals. How do you envision the collaboration process for this project? Sincerely, Sean

@CyberSasu

Hi, I’m Saswata Mukhopadhyay. I can help with AI/ML development, including model building, data processing, prediction systems, and integration with applications or devices. I focus on practical, reliable solutions and proper implementation based on project needs. Share your requirement, and I’ll be happy to review it and suggest the best approach.

@Kyybaindia

# Chennai-based company# Hi Sam, This is Shree from Kyyba. We are a Chennai-based company. Happy to allocate our developers to work in your location or remotely from the office. For Generative AI, we design domain-specific solutions using prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, and model fine-tuning/adaptation where required. We have hands-on experience with both proprietary models (OpenAI GPT, Gemini, and Azure OpenAI) and open-source LLMs (LLaMA and Mistral) to optimize cost, latency, and performance. For Agentic AI, we architect intelligent multi-agent systems using frameworks such as LangChain, LangGraph, and CrewAI, enabling planning, reasoning, memory, and task orchestration for complex workflows. From an engineering perspective, we follow a cloud-native, API-first architecture with Python, PyTorch, TensorFlow, Scikit-learn, containerization via Docker, and deployment on AWS SageMaker, Vertex AI, or Azure ML depending on ecosystem fit. Data persistence can be managed using PostgreSQL, DynamoDB, and vector stores. Based on the job description, we can share suitable profiles. Please open a private freelancer chat to discuss further It is really appreciated. Could you please let me know whether you are an individual or a company? Thanks Shree Kyyba

@smendoza

Hello, I have experience in all you mentioned with exception of CrewAI, Vertex AI and SageMaker. I am available to discuss the project when you are ready and am available to start immediately. Regards, Stefany M.

@shantanugupta022

I am suited for this task as I have worked with the required tech stack and have hands on experience with projects .

@akum1104

Hello, I am highly interested in this Principal AI Engineer opportunity and believe my experience aligns strongly with your requirements around Generative AI, Agentic AI systems, and scalable enterprise-grade AI product development. I have hands-on experience designing AI-driven platforms leveraging LLMs, RAG architectures, prompt engineering, workflow orchestration, and intelligent automation systems. My expertise includes working with OpenAI GPT models, vector databases, NLP pipelines, AI agent frameworks, and cloud-integrated AI ecosystems. I have also been actively involved in developing AI-powered solutions focused on healthcare, insurance, analytics, and enterprise transformation use cases. From a technical standpoint, I am comfortable working across Python-based AI stacks, API integrations, scalable backend architectures, cloud services, and data-driven applications. I understand the importance of building production-ready AI systems with reliability, scalability, observability, and business alignment in mind. Additionally, I bring strong consulting and stakeholder management experience, enabling me to operate effectively in ambiguous and fast-paced environments while translating business challenges into practical AI solutions. I would welcome the opportunity to discuss your project requirements further and contribute toward building impactful next-generation AI systems. Best Regards, Ashutosh Kumar