Position Description :

We are looking for a Lead AI Developer to join our team. This position involves designing, developing, and leading enterprise-grade Generative AI solutions. This role requires deep hands-on expertise in Python, Large Language Models (LLMs), prompt engineering, embeddings, vector databases, and agent-based architectures.

Responsibilities :

- Lead the design and development of scalable Generative AI solutions using LLMs

- Define and implement architectures for RAG, agentic, multi-agent, and multimodal systems

- Review and guide solution designs to ensure alignment with AI CoE standards and enterprise architecture

- Mentor and guide developers on GenAI patterns, tools, and best practices

- Develop end-to-end AI solutions using Python

- Design, test, and optimize prompt engineering strategies

- Build and manage embeddings, vector search, and semantic retrieval pipelines

- Develop applications using LangChain and monitor, evaluate, and debug workflows using LangSmith

- Integrate LLMs with enterprise systems, APIs, and structured/unstructured data sources

- Implement agent-based and multi-agent workflows for task orchestration and automation

- Work with multimodal models involving text, documents, and images

- Work closely with the AI CoE to align on AI frameworks, reusable components, and architectural standards

- Contribute to enterprise GenAI accelerators, reference architectures, and best practices

- Support governance, security, responsible AI, and compliance requirements

- Share learnings, patterns, and improvements across teams to drive consistency and adoption

- Apply deep understanding of LLM capabilities, limitations, and optimization techniques

- Implement techniques such as RAG, tool/function calling, memory, and agents

- Evaluate and recommend appropriate models (OpenAI, Azure OpenAI, open-source models)

- Ensure performance, scalability, cost optimization, and reliability of AI solutions

Must-Have Skills :

- Strong programming experience in Python and API development using FastAPI or similar frameworks.

- Hands-on experience with OpenAI, Azure OpenAI, Anthropic Claude, and Google Gemini.

- Build AI applications using LangChain, LangGraph, OpenAI Agents SDK, and Semantic Kernel.

- Design and implement Agentic AI solutions, including multi-agent workflows, tool orchestration, memory management, and human-in-the-loop capabilities.

- Develop RAG solutions using embeddings, chunking, vector search, reranking, and enterprise knowledge retrieval.

- Experience with Model Context Protocol (MCP), function calling, and enterprise tool integrations.

- Hands-on experience with Vector Databases such as Pinecone, ChromaDB, pgvector, FAISS, or Azure AI Search.

- Strong knowledge of Prompt Engineering, Context Engineering, prompt optimization, and AI evaluation techniques.

- Experience with relational and NoSQL databases (PostgreSQL, MongoDB, Redis) and enterprise data integration.

- Strong analytical, problem-solving, debugging, communication, and stakeholder management skills.

Good-to-Have Skills :

- Deploy and manage AI applications on AWS, Docker, and OpenShift/Kubernetes using CI/CD pipelines.

- Build secure, scalable AI solutions with monitoring, guardrails, observability, and Responsible AI practices.

- Collaborate with product, architecture, and engineering teams to deliver AI-powered enterprise solutions.