Applied AI Engineer (Agentic AI & ML)

Role Overview We are seeking a Applied AI Engineer to embed directly with our business units and thermal-asset operations teams and own AI solutions end-to-end — from problem discovery through production. This is a builder's role, not an advisory one: you will sit with operators and domain experts, scope where AI can remove real cost or risk, write the production code, deploy it, and stay accountable for it running reliably. The role combines two demands that rarely sit together: a strong machine-learning foundation (you will maintain and improve models that run our assets) and hands-on agentic AI engineering. The ideal candidate is delivery-oriented, comfortable with ambiguity, and motivated by business impact over benchmarks. Key Responsibilities Discover & scope • Embed with business and operations stakeholders to identify high-value AI use cases and decompose ambiguous problems into deliverable solutions Build agentic AI systems • Design and build production-grade agentic AI solutions using LLMs, prompt engineering, RAG, and tool/function calling • Architect multi-agent workflows and agent orchestration, including MCP (Model Context Protocol) servers, sub-agents, and custom integrations into enterprise systems • Build secure, scalable backend APIs and services (C# / .NET) to support AI workloads Maintain & enhance ML/DL models • Own, maintain, and improve production ML/DL models • Retrain, evaluate, and tune models as data and operating conditions evolve Deploy & operate in production • Deploy and operate applications and models on Microsoft Azure/GCP behind production auth, logging, and monitoring • Build evaluation frameworks, guardrails, and observability for non-deterministic AI systems; own reliability, performance, cost, and security • Implement CI/CD pipelines and follow DevOps best practices • Codify & feed back • Turn bespoke builds into reusable, repeatable internal patterns and components • Route field learnings back into platform, tooling, and roa…