Generative AI Engineer

GenAI Developer/Engineer

Experience: Total Experience: 10-15 years, AI-ML and GenAI Experience: 4–6 years

Strong fundamentals and real‑world production exposure. Responsible for building, deploying, and operating GenAI solutions using modern agentic architectures.

Core Responsibilities

• Design, develop, and deploy GenAI and ML solutions using Azure OpenAI, Azure ML, Azure AI Studio , and related services
• Build LLM‑powered applications using: Prompt engineering, Tool/function calling, Retrieval‑Augmented Generation (RAG) and Agentic AI design patterns
• Develop Agent‑to‑Agent (A2A) workflows for task decomposition, orchestration, and collaboration
• Implement Model Context Protocol (MCP) to standardize tool access, memory, and context management across agents
• Apply MLOps and LLMOps practices:
• CI/CD for models and prompts and Versioning, monitoring, evaluation
• Failure detection and mitigation (hallucinations, drift, prompt regressions)
• Implement LLM security and guardrails :
• Prompt injection prevention,
• Data leakage protection and Output moderation and policy enforcement
• Fine‑tune and adapt models for economy - token economics, latency, and cost optimization
• Build and expose AI capabilities via secure APIs and microservices
• Collaborate with architects, data engineers, and platform teams
• Actively research and document emerging GenAI techniques and tools

Mentor junior engineers and contribute to internal knowledge assets

Technology Stack

• Programming : Python (PEP8 / Google Python standards)
• LLMs : Azure OpenAI, OpenAI, open‑source and local LLMs
• AI Platforms : Azure ML, Azure Databricks, Azure AI Services
• Agent Frameworks : LangChain, LangGraph, Semantic Kernel (or equivalent)
• Protocols & Patterns : MCP, A2A, RAG variants
• DevOps : Docker, REST APIs, CI/CD pipelines
• Datastores : SQL‑based RDBMS, Vector databases
• Graph databases: Neo4j, GraphDB

Soft Skills

• Strong ownership mindset and delivery focus
• Ability to work in agile, fast‑changing environments

Effective collaboration across engineering and architecture teams