Python AI Senior Developer

Role & 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

Preferred candidate profile

• 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.