Summary of the position

We are seeking an experienced and visionary Practice Lead – AI & Data Engineering to build, scale, and lead our AI and Data Engineering practice as a strategic capability within the organisation.

The role will define the technical vision, delivery standards, talent strategy, service offerings, and commercial roadmap for the practice. While our current engagements include Microsoft Fabric, Azure AI, GenAI and ML, the role is technology-agnostic and expected to continuously evolve with emerging technologies, market trends, and client needs.

This is a strategic leadership role combining technical expertise, architecture governance, delivery excellence, people leadership, client advisory, and commercial acumen.

Key accountabilities

Practice Strategy & Vision
- Define and own the AI, ML and Data Engineering practice roadmap, including technology direction, service offerings, accelerators, and reusable frameworks.
- Evaluate emerging cloud and open-source platforms including Azure, AWS, Microsoft Fabric, Databricks, Snowflake, and their AI/ML capabilities.




- Establish the practice as a Centre of Excellence (CoE) for data engineering, analytics enablement, AI/ML development, deployment, and governance.
- Develop reusable accelerators for data engineering and AI/ML use cases, including RAG pipelines, prompt libraries, model templates, feature engineering, and MLOps/LLMOps patterns.
- Align practice strategy with business growth objectives, client needs, and evolving trends such as GenAI and AI-driven decision-making.

Technical Leadership & Architecture Governance
- Act as the technical authority across data engineering and AI/ML engagements.
- Define reference architectures, engineering standards, and best practices covering:
- Data lakes, lakehouses and data warehouses
- ETL/ELT, batch, streaming and real-time pipelines
- Data quality, observability, metadata, lineage and governance
- Feature engineering, model development, serving and monitor

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