Generative AI Engineer (Multi-Agent AI & RAG)
📍 Location: Hyderabad / Bangalore / Noida / Gurgaon
🏠 Work Model: Hybrid / Remote
📅 Working Days: 5 Days a Week
💼 Experience: 6+ Years
Role Overview
We are seeking an experienced Generative AI Engineer with strong expertise in Multi-Agent AI Systems and RAG (Retrieval-Augmented Generation) Architecture. The ideal candidate should have hands-on experience designing, developing, and deploying enterprise-grade AI solutions powered by LLMs, agent orchestration, semantic retrieval, and intelligent automation.
Key Responsibilities
- Design and develop enterprise-scale Multi-Agent AI solutions.
- Build and optimize RAG architectures, including ingestion, chunking, embeddings, vector indexing, retrieval, and grounded response generation.
- Develop agent workflows involving orchestration, tool calling, routing, memory, and decision-making.
- Integrate AI applications with enterprise systems, APIs, databases, and knowledge repositories.
- Implement prompt engineering, evaluation mechanisms, guardrails, and hallucination-reduction strategies.
- Build scalable backend services and APIs for AI-powered applications.
- Deploy, monitor, and optimize AI solutions in cloud environments.
Mandatory Requirements
- 6+ years of overall IT experience.
- Strong experience in Generative AI and LLM-based applications.
- Hands-on Multi-Agent Development experience (Mandatory).
- Hands-on RAG Architecture experience (Mandatory).
- Experience with vector databases, embeddings, semantic search, and retrieval systems.
- Strong proficiency in Python and API development.
- Experience building and deploying production-grade AI solutions.
Preferred Qualifications
- BE/BTech, ME/MTech, or MS from a reputed institution.
- Experience with enterprise AI, agentic workflows, and large-scale knowledge systems.
- Exposure to cloud platforms, containerization, CI/CD, and AI observability.
Ideal Candidate
- Has built real-world Multi-Agent systems, not just supporting tools or integrations.
- Has deep expertise in RAG implementation and optimization.
- Can independently architect, develop, and deploy enterprise GenAI solutions.