Home/Jobs/LLM Operations Engineer LLM Operations Engineer Accenture Pune 3-8 years Today $20.5K–32.5K/yr Full-time Onsite Skills Required LLM Gen AI Agentic AI LLMOps RAG Prompt Engineering OpenAI Hugging Face LangChain Azure OpenAI RAGAS Machine Learning Operations MLOps Multi-Agent Systems Python Description We are looking for a highly skilled AI engineer specializing in Generative AI and multi-agent systems to design and deploy intelligent, autonomous solutions. The role focuses on LLM-powered, agent-driven architectures for decision-making, orchestration, and autonomous execution across enterprise systems.

Role: LLM Operations Engineer Location: Pune Experience Minimum 3 years of experience

3–8 years' experience in AI/ML with strong focus on Generative AI

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

Domain experience in industrial, energy, or IoT environments Qualification 15 years full time education Responsibilities Design and build multi-agent AI systems capable of planning, reasoning, and task execution

Develop applications using LLMs with advanced prompt engineering and orchestration

Implement Agentic workflows including planner-executor-critic-memory loops

Build RAG 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 using vector stores and knowledge graphs

Design multi-agent collaboration protocols such as hierarchical, swarm, and role-based agents





Deploy scalable solutions using MLOps and LLMOps practices, including monitoring, evaluation, and guardrails

Ensure AI safety, governance, and responsible AI practices

Communicate with business stakeholders to translate workflows into agent pipelines

Balance latency, cost, and accuracy in LLM systems Additional Responsibilities Utilize cloud-native services and tools for scalable and efficient deployment

Monitor LLM performance and address operational challenges Nice To Have Azure OpenAI preferred; AWS and GCP acceptable More Skills Large Language Models (LLMs), Hugging Face Transformers, AutoGen, CrewAI, Semantic Kernel, FAISS, Pinecone, Weaviate, AWS, GCP, Docker, Kubernetes, CI/CD pipelines, prompt optimization, chain-of-thought reasoning, tool augmentation, context management, token optimization, vector databases, knowledge graphs, ReAct, Plan-and-Execute, Tree of Thought Other Project Role Description: Utilize cloud-native services and tools for scalable and efficient deployment

Required Skill: Generative AI, Machine Learning Operations, Large Language Models (LLMs), Agentic AI

Valuable to have skills: NA

This position is based at the Pune office Prepare for this role Recommended resources to build the skills for this position. Sponsored. Generative AI with Large Language Models Coursera Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

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