Artificial Intelligence Engineer - 12 Months Contract

The job holder is responsible for designing, building, and operating production-grade AI agents on Microsoft Foundry — LLM-powered systems that reason, use tools, and orchestrate multi-step workflows against enterprise data and services. This is a hands-on, pro-code role: the engineer writes Python, integrates Azure services, and ships agentic applications that drive measurable business impact. The engineer works within agile teams alongside Product, Data Science, and Architecture to take agent use cases from prototype to production.Key Responsibilities:AI Agent Development on Microsoft FoundryDesign, build, and deploy pro-code AI agents on Microsoft Foundry, leveraging its agent framework, model catalogue, and tool/function-calling capabilities.Integrate agents with Palantir Foundry via MCP (Model Context Protocol) to fetch enterprise data and execute Ontology actions, alongside other tool, retrieval, memory, and multi-agent orchestration patterns in Python.Build evaluation harnesses and guardrails (groundedness, safety, cost, latency) and iterate on prompts, tools, and architectures based on measured outcomes.LLM Engineering & ProductionizationApply prompt engineering, structured output, RAG, and grounding techniques appropriate to each use case.Operate agents in production: observability, tracing, cost monitoring, regression testing, and incident response.Manage versioning of prompts, tools, and agent configurations alongside code.Azure & Platform IntegrationIntegrate with Azure services (Azure OpenAI, AI Search, Storage, Functions, Container Apps, Key Vault, Entra ID) to deliver secure, scalable agent solutions.Build and maintain CI/CD pipelines for agent deployment, including automated evals as a release gate.Implement authentication, authorization, secret management, and data-handling patterns aligned with enterprise security standards.Cross-functional CollaborationWork closely with Product Managers, Data Scientists, and IT Architects to translate agent use cases into delivery plans.Communicate trade-offs (model choice, latency, cost, accuracy) clearly to technical and non-technical stakeholders.Champion agentic patterns and reusable components across the engineering organization.Qualifications:Software engineering experience with hands-on production experience building LLM-based / agentic systems (tool use, orchestration, RAG, evals).Bachelor’s Degree in Computer Science, Engineering, or related quantitative fieldAdvanced degrees preferred.Strong Python — idiomatic, typed, tested; comfortable with async and modern packagingProven experience building production AI agents in code (e.g. Microsoft Foundry / Azure AI Foundry, Semantic Kernel, AutoGen, LangGraph, LlamaIndex, OpenAI Agents SDK, or equivalent custom frameworks)Microsoft Foundry experience strongly preferred (agent authoring, model catalog, connectors, deployment)Azure exposure — Azure OpenAI, AI Search, identity, storage, serverless compute; familiarity with Azure DevOps or GitHub ActionsDeep working knowledge of LLM application patterns: tool/function calling, structured outputs, RAG, memory, multi-agent orchestrationExperience with evaluation frameworks and observability for LLM systems (tracing, prompt/response logging, automated evals)DevOps fundamentals: Git, CI/CD, containers, infrastructure-as-code; exposure to data engineering and vector stores is a plusHigh-energy, ownership-driven mindsetStrong problem-solving and analytical skillsExcellent communication and collaboration abilitiesComfortable navigating ambiguity and driving clarityExperience in agile/start-up environments preferredTrack record of taking AI/ML or agent projects from prototype to production