Data Engineer
Summary
Designs and implements AI-powered agents/copilots using Copilot Studio and Azure AI Foundry, focusing on hybrid architectures, RAG pipelines, and enterprise AI integration for scalable, production-grade solutions.
- Solution Design & Engineering
- Design and implement AI-powered agents and copilots using Copilot Studio.
- Develop advanced AI solutions and orchestration pipelines using Azure AI Foundry.
- Build hybrid architectures combining Copilot Studio (front-end interaction) and Foundry (backend intelligence).
- Implement multi-step reasoning workflows and agent orchestration for complex use cases.
- Data Integration & AI Enablement
- Integrate AI solutions with enterprise systems using:
- APIs, connectors, and microservices
- Data platforms (e.g., enterprise data lakes, SQL, graph DBs)
- Develop and optimise Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge use cases.
- Agent Development & Deployment
- Build and deploy scalable AI agents across business domains.
- Enable multi-channel deployment (e.g., Microsoft Teams, Power Platform).
- Ensure robust testing, validation, and performance optimisation prior to production release.
- Governance, Quality & Standards
- Apply best practices across:
- Security (RBAC, managed identities, Key Vault)
- Data governance and compliance
- Responsible AI and risk mitigation
- Conduct peer reviews on solution design and code and guide team adoption of standards.
- Performance Optimisation & Monitoring
- Optimise model usage, cost, and response accuracy.
- Implement observability (logging, monitoring, tracing).
- Diagnose and resolve issues across CopilotFoundry integrations.
- Coaching & Enablement
- Mentor squad members and facilitate knowledge transfer.
- Support capability uplift across the AI squad and broader CIB community.
- Contribute to reusable patterns, frameworks, and playbooks. Experience & Qualifications Mandatory Experience
- 13 years hands-on experience in:
- Microsoft Copilot Studio
- Microsoft Azure AI Foundry
- Other AI Tools / Platforms
- Proven experience building and deploying AI/ML or generative AI solutions
- Experience in agent-based architectures or conversational AI
- Python (preferred), API integration
- AI/LLM capabilities:
- Prompt engineering
- RAG pipelines
- Model evaluation and tuning
- Microsoft Azure ecosystem
- Data integration (SQL, APIs, data lakes)
- Tooling:
- GitHub, CI/CD pipelines, DevOps practices / SAFe / Agile
- Strong problem-solving and analytical thinking
- Ability to design scalable, production-grade AI solutions
- Excellent communication and stakeholder engagement
- Commitment to quality, governance, and standards
- Continuous learning mindset in emerging AI technologies
Programming: