Must Have Technical/Functional Skills
GEN AI & AGENTIC AI build & Implementation experience
- 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