ML / AI Engineer

Responsibilities:

• Understand the business need before building and take solutions from discovery through production.
• Design, build, deploy and continuously improve enterprise-grade agentic AI applications.
• Build agents that reason across multiple steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop workflows.
• Design and implement RAG pipelines covering ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.
• Build MCP-based integrations and connect agents to backend systems through REST/OpenAPI, webhooks and event-driven patterns with secure authentication.
• Apply structured LLM patterns including tool calling, schema-validated outputs, retries, fallbacks and guardrails.
• Coordinate with Business Product Owners, AI Value Architects and other squads.
• Own end-to-end delivery of significant AI use cases and set the pace on quality, evaluation and reliability.

Requirements:

• Degree in Computer Science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience.
• 5+ years building production-grade software, including 3+ years with GenAI and LLMs.
• Minimum 1 year of hands-on Agentic AI experience with a proven record of taking agentic solutions to production.
• Hands-on experience or strong working knowledge of MCP for connecting agents to tools, systems, APIs and data.
• Strong Python with working knowledge of TypeScript / JavaScript. Java or C# is a plus.
• Experience with async programming, FastAPI, Pydantic, Git, CI/CD, testing, error handling and logging.
• Experience with at least one agent framework or enterprise AI platform such as LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore or Google Vertex/Gemini.
• Experience with vector databases/search platforms such as Azure AI Search, pgvector, Pinecone, Weaviate or OpenSearch.