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
- Develop tool-using agents that integrate with APIs, databases, and enterprise systems
- Architect and deploy AI copilots and autonomous assistants for business workflows
- Optimize LLM performance using fine-tuning, prompt chaining, and caching strategies
- Implement short-term and long-term memory mechanisms (vector stores, knowledge graphs)
- Design multi-agent collaboration protocols (hierarchical, swarm, role-based agents)
- Deploy scalable solutions using MLOps + LLMOps practices (monitoring, evaluation,

guardrails)

- Ensure AI safety, governance, and responsible AI practices Required Skills & Competencies
- Bachelor’s/master’s in computer science, AI, or related field
- 3–8 years experience in AI/ML with strong focus on Generative AI
- Strong Python development skills
- Hands-on experience with:

o LLMs & GenAI frameworks: OpenAI, Hugging Face Transformers o Agent frameworks: LangChain, AutoGen,



CrewAI, Semantic Kernel o RAG pipelines & vector DBs: FAISS, Pinecone, Weaviate

- Experience building API-driven, tool-integrated AI agents
- Solid understanding of:

o Prompt engineering & prompt optimization o Chain-of-thought reasoning and tool augmentation o Context management and token optimization

- Experience with cloud platforms (Azure OpenAI preferred, AWS/GCP acceptable)
- Knowledge of Docker, Kubernetes, CI/CD pipelines
- Systems thinking for designing autonomous AI architectures
- Strong problem decomposition for agent task design
- Ability to balance latency, cost, and accuracy in LLM systems
- Communication with business stakeholders to translate workflows into agent pipelines
- Innovation mindset with focus on applying agentic AI in production Preferred Qualifications
- Experience building multi-agent orchestration systems with role-based coordination
- Exposure to agent planning algorithms (ReAct, Plan-and-Execute, Tree-of-Thought)
- Experience with LLM evaluation frameworks (RAGAS, TruLens, Promptfoo)
- Knowledge of graph-based reasoning / knowledge graphs
- Building autonomous systems or copilots in enterprise environments
- Domain experience in industrial, energy, or IoT environments Tech Stack (Modern GenAI Stack)
- Languages: Python
- Frameworks: LangChain, CrewAI, AutoGen, Semantic Kernel
- LLMs: OpenAI GPT, Azure OpenAI, Claude, Llama
- Vector DB: Pinecone, Weaviate, FAISS
- Orchestration: Airflow, Prefect
- Deployment: Docker, Kubernetes
- Cloud: Azure AI Studio / Azure ML (preferred) KPIs / Success Metrics
- Autonomous task completion rate of agents
- Reduction in manual workflows via AI automation
- Latency and cost optimization of LLM pipelines
- Accuracy and reliability of agent outputs
- Adoption rate of AI copilots across teams

📌 Artificial Intelligence Engineer (Bengaluru)
🏢 NextHire
📍 Bengaluru