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 roadmap decisions

Required Skills

Machine Learning / Deep Learning (mandatory)

. Demonstrated hands-on experience building, training, evaluating, and deploying ML/DL models in production

. Solid ML fundamentals: evaluation, training, problem decomposition

. Experience with forecasting, predictive maintenance, or time-series modelling is strongly preferred

Applied & Agentic AI (mandatory)

. Hands-on experience with LLMs and prompt engineering

. Experience building agentic AI workflows and agent orchestration

. Working knowledge of MCP, RAG, vector databases, and LLM orchestration frameworks

. Understanding of production AI challenges: evals, guardrails, hallucination/quality control, model drift, observability

Backend

. NodeJS

. Python

. MCP

. REST API design and integration

Cloud & DevOps

. Microsoft Azure proficiency (mandatory) - App Services, Azure OpenAI, Functions, Storage, etc.

. Azure DevOps CI/C

. Docker (AKS is a plus)

Good to Have

. Google Cloud Platform (GCP)

. Full-stack development experience (frontend + backend)

. Frontend skills (React, Flutter)

. Python or Node.js for AI/ML orchestration

. Experience integrating AI into enterprise/industrial or operational technology systems

. Exposure to AI-assisted development tools and workflows

. Background in energy, utilities, or asset-heavy industries

Mindset & Soft Skills

. Strong ownership: takes a problem from ambiguity to production and stays accountable for the outcome

. Translates business and operational problems into practical AI/ML solutions

. Comfortable working embedded with technical and non-technical stakeholders

. Clear communicator across engineering, operations, and business audiences

. Thrives in a dynamic environment with evolving objectives and direct user iteration

Applied AI Engineer (Agentic AI & ML)