AI Engineer

AI Solution Development
Design and develop AI applications, copilots, and multi-agent systems aligned with approved business requirements. • Build AI-powered assistants using Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Graph, Power Platform and Azure Services. • Develop reusable AI components and shared frameworks. • Translate user stories and functional requirements into working AI solutions.

• Agentic AI Engineering
Design and develop multi-agent systems using approved architecture. • Implement orchestration frameworks, agent collaboration patterns, tool calling, memory management, and human-in-the-loop controls. • Design agent interfaces, workflows, guardrails, and monitoring capabilities. • Participate in architecture reviews and technical governance.

• Prompt Engineering & Model Development
Create, test, optimize, and document prompts. • Evaluate AI model performance. • Develop Retrieval-Augmented Generation (RAG) solutions. • Implement vector search and semantic retrieval capabilities. • Optimize accuracy, latency, and cost.

• Data & Integration Engineering
Integrate AI products with enterprise systems including SAP S/4HANA, JD Edwards, Planview, SharePoint, Teams, Microsoft 365 and engineering platforms. • Design APIs and data pipelines. • Ensure secure handling of enterprise data.

• Cloud & Platform Engineering
Deploy solutions on approved cloud environments. • Implement CI/CD pipelines. • Configure
monitoring, logging, and alerting. • Support infrastructure automation and environment management.

• LLMOps / MLOps
Establish deployment pipelines for AI products. • Manage AI lifecycle activities: development, testing, validation, deployment, monitoring and retirement. • Monitor token consumption and cloud costs. • Implement model governance and version control.
• Security, Risk & Compliance
Ensure compliance with Group AI Policy, Energy AI Governance SOP, cybersecurity standards, data privacy requirements and EU AI Act requirements. • Participate in risk assessments and architecture reviews. • Support audit and compliance activities.

• Quality Assurance
Conduct model testing and validation. • Measure accuracy and business performance. • Execute AI evaluation frameworks. • Document known limitations and mitigation actions.
• Documentation & Knowledge Management

Maintain technical documentation. • Create architecture diagrams and deployment guides. • Produce support documentation and training materials. • Ensure solutions can be maintained independently of individual developers.

• Adoption & Continuous ImprovementSupport pilots, Proofs of Concept (PoCs), and production rollouts. • Troubleshoot production issues. • Analyze usage patterns and user feedback. • Recommend enhancements that improve business outcomes.