Generative AI Engineer

Job Title:

Experience:

About the Role

Generative AI Engineer

MCP

Key Responsibilities

LLM Applications & Agentic Frameworks

• Design and implement end-to-end LLM applications using OpenAI, Claude, Mistral, Gemini, or LLaMA on AWS, Databricks, Azure or GCP.
• Build intelligent, autonomous agents using LangGraph, AutoGen, LlamaIndex, Crew.ai, or custom frameworks.
• Develop Multi Model, Multi Agent, Retrieval-Augmented Generation (RAG) applications with secure context embedding and tracing with reports.
• Rapidly explore and showcase the art of the possible through functional, demonstrable POCs

Advanced AI Experimentation

• Fine-tune LLMs and Small Language Models (SLMs) for domain-specific use.
• Create and leverage synthetic datasets to simulate edge cases and scale training.
• Evaluate agents using custom agent evaluation frameworks (success rates, latency, reliability)
• Evaluate emerging agent communication standards —

  A2A

  (Agent-to-Agent) and

  MCP

  (Model Context Protocol)

Business Alignment & Cross-Team Collaboration

• Translate ambiguous requirements into structured, AI-enabled solutions.
• Clearly communicate and present ideas, outcomes, and system behaviors to technical and non-technical stakeholders

Good-To-Have

• Microsoft Copilot Studio
• DevRev
• Codium
• Cursor
• Atlassian AI
• Databricks Mosaic AI

Qualifications

• 6–9 years of experience in software development or AI/ML engineering
• At least 3 years working with LLMs, GenAI applications, or agentic frameworks.
• Proficient in AI/ML, MLOps concepts, Python, embeddings, prompt engineering, and model orchestration
• Proven track record of developing

  functional AI prototypes

  beyond notebooks.
• Strong presentation and storytelling skills to clearly convey GenAI concepts and value.
• Ability to independently drive AI experiments from ideation to working demo.