Posted 11 June, 2026
AI Engineer & Sr. AI Engineer
NR Consulting - India
Bangalore, RMZ Bellandur, IN
Full Time
Reference: 26-06329-2220-1
Title: AI Engineer & Sr. AI Engineer
Location: Bangalore, RMZ Bellandur
Exp: 4-9 years
Job Description:
AI Engineer – GenAI & Multi-Agent Systems
Role Overview
We are looking for a highly skilled AI Engineer specializing in Generative AI and Multi-Agent Systems to design and deploy intelligent, autonomous solutions. This role focuses on building LLM-powered, agent-driven architectures that can reason, collaborate, and execute complex workflows across enterprise systems.
You will work on cutting-edge Agentic AI frameworks, enabling systems that go beyond prediction to decision-making, orchestration, and autonomous execution.
Key Responsibilities
• Design and build multi-agent AI systems capable of planning, reasoning, and task execution
• Develop applications using LLMs (GPT, Claude, Llama, etc.) with advanced prompt engineering and orchestration
• Implement Agentic workflows (planner → executor → critic → memory loops)
• Build RAG (Retrieval-Augmented Generation) pipelines with vector databases for enterprise knowledge grounding
Location: Bangalore, RMZ Bellandur
Exp: 4-9 years
Job Description:
AI Engineer – GenAI & Multi-Agent Systems
Role Overview
We are looking for a highly skilled AI Engineer specializing in Generative AI and Multi-Agent Systems to design and deploy intelligent, autonomous solutions. This role focuses on building LLM-powered, agent-driven architectures that can reason, collaborate, and execute complex workflows across enterprise systems.
You will work on cutting-edge Agentic AI frameworks, enabling systems that go beyond prediction to decision-making, orchestration, and autonomous execution.
Key Responsibilities
• Design and build multi-agent AI systems capable of planning, reasoning, and task execution
• Develop applications using LLMs (GPT, Claude, Llama, etc.) with advanced prompt engineering and orchestration
• Implement Agentic workflows (planner → executor → critic → memory loops)
• Build RAG (Retrieval-Augmented Generation) pipelines with vector databases for enterprise knowledge grounding
