Must Have Technical/Functional Skills

Hands-on experience with LLMs and RAG architectures
Experience with vector databases and semantic search
Practical understanding of prompt engineering and evaluation
Hands-on experience building agentic or multi-step AI workflows (not just prompting)
Experience with orchestration frameworks and tool calling
Understanding of AI safety, guardrails, and reliability patterns
Ability to operationalize AI agents at enterprise scale

Roles & Responsibilities

Build end-to-end GenAI applications using LLMs (text, documents, Q&A, summarization, extraction)
Implement RAG pipelines: data ingestion, chunking, embeddings, vector search, and grounded responses
Develop APIs and microservices to integrate GenAI solutions with enterprise systems
Implement structured outputs, function calling, and prompt frameworks
Support evaluation, testing, monitoring, and continuous improvement of GenAI solutions
Collaborate closely with the GenAI Architect and Agentic AI Engineers
Design and implement agentic AI workflows (planning → execution → observation → iteration)
Build tool-using AI agents integrated with APIs, data sources, and enterprise systems
Implement multi-agent or orchestrated agent architectures where required
Combine Agentic AI with RAG for grounded, enterprise-safe responses
Implement guardrails, safety checks, and evaluation mechanisms
Debug, optimize, and operationalize agentic workflows in production