LLM / Agentic AI Engineer

Role Overview

strategic and hands-on role

Key Responsibilities

• Design, fine-tune, and evaluate

  Large Language Models (LLMs)

  for agent-based reasoning, natural language interaction, and workflow orchestration.
• Build

  autonomous agents

  using frameworks like

  LangChain

  ,

  AutoGen

  ,

  CrewAI

  , or similar, integrated with tool use, memory, and multi-step planning.
• Implement

  Agentic Frameworks and Digital Twins

• Develop and optimize

  Retrieval-Augmented Generation (RAG)

  pipelines using

  vector databases

  (e.g., FAISS, Weaviate) and hybrid architectures like

  GraphRAG

  .
• Engineer agent workflows with

  tool calling

  ,

  multi-agent collaboration

  , and

  long-term memory (episodic + semantic)

  .
• Monitor and improve performance metrics like reasoning accuracy, hallucination rate, and latency under production constraints.
• Collaborate with DevOps, product, and platform teams to scale agents using Docker, Kubernetes, and Azure ML.

Must-Have Skills

• 3–6 years of experience in

  LLM-based NLP / AI

  roles, with hands-on deployment experience.
• Proficiency in

  LangChain

  ,

  AutoGen

  ,

  CrewAI

  , or similar agent frameworks (real-world examples preferred).
• Strong understanding of

  agent memory

  ,

  task planning

  , and

  function/tool calling workflows

  .
• Experience with

  RAG pipelines

  ,

  embedding models

  , and

  vector search systems

  .
• Deep knowledge of

  Transformers

  ,

  Hugging Face ecosystem

  , and custom prompt engineering.
• Proficiency in

  Python

  and ML libraries like PyTorch, TensorFlow.
• Familiarity with

  knowledge graphs

  ,

  enterprise data systems

  , and orchestration strategies.

Preferred Qualifications

• Experience designing

  persona-aware agents

  for enterprise use cases (BFSI, healthcare, public sector).
• Familiarity with

  BertGraph

  ,

  GraphRAG

  , or similar graph-augmented architectures.
• Experience with

  Azure AI Studio

  ,

  OpenAI APIs

  ,

  Anthropic Claude

  , or

  Meta LLaMA

  models.
• Background in agent evaluation: using metrics for tool use accuracy, conversation consistency, and action success rates.