Senior AI Deployment Engineer

5+ years of software engineering with meaningful production ownership.

Has built and shipped LLM-powered systems that ran in production.

Production-grade Python as your primary language.

LLM-powered systems in production: real-world experience working with models via APIs - harnesses, orchestration, structured output, tool use and agents, RAG where appropriate.

Evals and quality: designing evaluation sets, measuring accuracy, recall and precision for LLM steps, regression-testing prompts and workflows.

Safety and guardrails in practice: PII handling, data-boundary discipline, prompt-injection awareness, human-in-the-loop design.

Cost and performance optimisation: model selection, caching, batching, token economics, latency budgets.

Deployment and operations: CI/CD, containerisation, monitoring and alerting for AI workloads.

Data processing fundamentals: pipelines, transformation, validation, anomaly handling.

Customer- or stakeholder-facing delivery experience: consultancy, forward-deployed engineering, solutions engineering, or embedded or platform roles serving non-engineering users.

Experience with Azure and/or .NET. We host on Azure and our core stack is .NET, so willingness to integrate with both is required; existing expertise is a bonus rather than a requirement.

Experience with agent hosting and sandboxing platforms, LLM or MCP gateways, or agentic workflow orchestration tooling.

Experience in financial services or another regulated environment.

Experience deploying models built by a data science team into customer-facing production systems.