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

📌 Python AI Senior Developer (Hyderabad)
🏢 VLink
📍 Hyderabad