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Applied AI Engineer (Agentic AI & ML)
Job Location:
Monthly Salary:
Experience Required:
Posted:
24 June 2026 (30+ days ago)
Posted:
Application Deadline:
21 September 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Role Overview
We are seeking a Forward Deployed 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 builders 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 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
Required Skills:
Applied AI Engineer (Agentic AI & ML)
Required Education:
degree / diploma
