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

Additional Responsibilities

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

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