Data Engineer at Mindworx Consulting – Johannesburg | Jobyssey

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

Technical Skills
Programming:

Python (preferred), API integration
AI/LLM capabilities:
Prompt engineering
RAG pipelines
Model evaluation and tuning

Cloud & Data:

Microsoft Azure ecosystem
Data integration (SQL, APIs, data lakes)
Tooling:
GitHub, CI/CD pipelines, DevOps practices / SAFe / Agile

Key Competencies

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

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