Senior Agentic AI Developer

Responsibilities

• Design, build, and ship agentic AI applications on Azure: multi-step reasoning, tool and function calling, retrieval, memory, and human-in-the-loop checkpoints.
• Implement retrieval-augmented generation pipelines, including chunking strategy, embedding and indexing, hybrid and semantic search, reranking, and grounding with citations.
• Integrate agents with client systems through APIs, databases, and Model Context Protocol (MCP) servers, writing the tool definitions and schemas the model depends on.
• Build evaluation harnesses and golden datasets, and treat accuracy, groundedness, and task completion as measured numbers rather than impressions.
• Instrument agents for production: tracing, token and cost telemetry, latency budgets, failure and fallback paths, and alerting on quality regressions.
• Implement guardrails and responsible AI controls, covering prompt injection defense, content filtering, PII handling, output validation, and clear boundaries on what an agent is allowed to do without human approval.
• Tune cost and latency through model selection, prompt caching, context management, batching, and routing simple work to smaller models.
• Apply engineering discipline to AI code: version control, code review, automated testing, CI/CD, and prompt and model versioning.
• Containerize and deploy agent services, owning the build, release, scaling, and runtime configuration of what you ship.
• Set technical direction on client engagements and mentor junior engineers, establishing patterns and standards the wider team can follow.
• Work directly with clients to turn ambiguous business problems into scoped agent use cases, and be honest about what current models can and cannot do reliably.
• Partner with Project Managers, data engineers, and application teams to deliver end to end, and document what you build so others can operate it.