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Bengaluru, Karnataka, India
Onsite
As a Senior AI Engineer, you will lead the design, development, and deployment of complex GenAI and agentic AI solutions. You will own end-to-end delivery of intelligent workflows powered by LLMs, embeddings, vector databases, and orchestration frameworks. You will be expected to drive architectural decisions, mentor junior engineers, and collaborate closely with product, platform, and data teams to deliver scalable, secure, and high-performance AI capabilities.
Core Responsibilities
• Architect and deploy LLM-based solutions at scale (e.g., enterprise chat agents, document intelligence, domain-specific RAG)
• Design agentic workflows using LangChain, A2A protocols, or custom-built orchestration layers
• Develop and optimize prompt chaining logic, vector search strategies, and context management approaches (e.g., with MCP)
• Integrate solutions with cloud-native platforms (Databricks, Azure Foundry), APIs, and enterprise data ecosystems
• Lead technical design reviews, enforce best practices in code structure, testing, and observability
• Conduct performance evaluations, token cost optimizations, and maintain reusability across solutions
• Mentor Associate and AI Engineers through pair programming, design guidance, and code reviews
• Stay ahead of emerging GenAI tooling and make recommendations for adoption in our stack
Required Skills
• 4–6+ years of experience in AI/ML engineering, with minimum 2 years working on GenAI/LLM use cases
• Deep proficiency in Python, with fluency in libraries like OpenAI, Pydantic, Transformers, LangChain, FAISS, and Pandas
• Demonstrated experience in building and deploying LLM-integrated applications using OpenAI or similar APIs
• Strong knowledge of vector databases, prompt engineering strategies, and LLM context optimization
• Hands-on with Azure cloud stack, GitHub/Azure DevOps CI/CD, and microservice/API design
Preferred Skills
• Experience with MCP (Model Context Protocol) and A2A orchestration in a production environment
• Working knowledge of LLM evaluation techniques (latency, relevance, hallucination mitigation)
• Open-source GenAI project contributions or demonstrable GitHub repositories
• Exposure to Databricks, Azure Foundry, and integration with enterprise-scale platforms
• Familiarity with prompt safety, guardrails, and observability frameworks like Trace loop, Prompt layer, etc.