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Agent engineeringMid-levelTop pickNew

LLM Operations Engineer

Accenture in IndiaPune, Maharashtra, India (On-site)

Posted 1d ago/Full-time/Expires Nov 10, 2026

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Why it matters

The role owns production multi-agent architectures including planner-executor-critic-memory loops, API-integrated agents, collaboration protocols, RAG, evaluation, guardrails, and monitoring. It explicitly covers planning approaches such as ReAct and Plan-and-Execute, plus enterprise workflow deployment.

About the job

Project Role : LLM Operations Engineer

Project Role Description : Utilize cloud-native services and tools for scalable and efficient deployment. Monitor LLM performance, address operational challenges, and ensure compliance and security standards in AI operations.

Must have skills : Generative AI, Large Language Models (LLMs), Machine Learning Operations, Agentic AI

Good to have skills : NA

Minimum 3 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Summary

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.

Roles & 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

Professional & Technical Skills

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
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
3–8 years' experience in AI/ML with strong focus on Generative AI
Strong Python development skills
Hands-on experience with:
LLMs & GenAI frameworks - OpenAI, Hugging Face Transformers
Agent frameworks: LangChain, AutoGen, CrewAI, Semantic Kernel
RAG pipelines & vector DBs- FAISS, Pinecone, Weaviate
Experience building API-driven, tool-integrated AI agents
Strong understanding of -
Prompt engineering & prompt optimization
Chain-of-thought reasoning and tool augmentation
Context management and token optimization
Experience with cloud platforms (Azure OpenAI preferred, AWS/GCP acceptable)
Knowledge of Docker, Kubernetes, CI/CD pipelines

Additional Information

The candidate should have minimum 3 years of experience in Generative AI.
This position is based at our Pune office.
A 15 years full time education is required.

Stack

python
openai
hugging face transformers
langchain
autogen
crewai
semantic kernel
kubernetes

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