AI/ML Engineer - GenAI, Lang Graph, Azure ML, Databricks

Role & responsibilities

• Design, develop, and deploy AI-powered applications using Large Language Models (LLMs) and Generative AI frameworks.
• Build AI agents using LangGraph, AutoGen, CrewAI, or PydanticAI for workflow automation and intelligent decision-making.
• Develop and optimize Retrieval-Augmented Generation (RAG) solutions and prompt engineering strategies for enterprise use cases.
• Integrate LLMs such as GPT, Claude, and Llama into scalable business applications.
• Deploy and manage AI/ML models using Azure Machine Learning and Databricks platforms.
• Build and maintain REST APIs, Web Sockets, and event-driven architectures for real-time AI services.
• Implement ML Ops best practices, including model versioning, monitoring, CI/CD pipelines, and automated deployments.
• Collaborate with cross-functional teams to ensure secure, scalable, and reliable AI solutions in production environments.
• Write high-quality Python code and follow modern software development practices, SDLC processes, and Agile methodologies.
• Utilize development tools such as Git, Jenkins, Jira, Confluence, and AI-assisted coding tools to improve productivity and software quality.
• Monitor model performance, troubleshoot production issues, and continuously enhance AI solution effectiveness.
• Contribute to enterprise AI governance, security, compliance, and best practices for AI system lifecycle management.

Preferred candidate profile

• 6-8 years of experience in Artificial Intelligence, Machine Learning, or Data Science with strong hands-on development expertise.
• Minimum 1+ year of experience building Generative AI applications and working with LLMs in enterprise environments.
• Strong experience in LangGraph, CrewAI, AutoGen, or PydanticAI for AI agent development and orchestration.
• Hands-on expertise in RAG (Retrieval Augmented Generation), Prompt Engineering, Vector Databases, and LLM Workflows.
• Proficient in Python with strong coding, debugging, and software engineering practices.
• Experience with Azure Machine Learning, Databricks, and Cloud-based AI Deployments.
• Strong knowledge of MLOps/LLMOps, model versioning, monitoring, and CI/CD pipelines.
• Experience developing and integrating REST APIs, WebSockets, and Event-Driven Architectures.
• Familiarity with Git, Jenkins, Jira, Confluence, SDLC, and Agile methodologies.
• Experience using AI-assisted development tools such as GitHub Copilot, Windsurf, or Codeium.
• Good understanding of enterprise security, governance, compliance, and AI best practices.
• Excellent communication, stakeholder management, analytical thinking, and problem-solving skills.