Generative AI Engineer

Job Title: Generative AI Engineer Experience: 4-9 years

About the Role

We are seeking a Generative AI Engineer with 6-9 years of experience who can independently explore, prototype, and present the art of the possible using LLMs, agentic frameworks, and emerging Gen AI techniques. This role combines deep technical hands-on development with non-technical influence and presentation skills.

You will contribute to key Gen AI innovation initiatives, help define new protocols (like MCP and A2A) and deliver fully functional prototypes that push the boundaries of enterprise AI not just in Jupyter notebooks, but as real applications ready for production exploration.

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.