AI and Data Practice Lead

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 monitoring

• Vector databases, RAG, MCP, LLM and agentic architecture patterns

• MLOps/LLMOps, CI/CD, experiment tracking, model versioning and automated retraining

• Responsible AI, model explainability, fairness and governance

• Review solution designs to ensure scalability, security, performance, maintainability, and reliability.

• Ensure strong alignment between data engineering, analytics, and AI/ML initiatives.

Delivery Excellence & Quality Assurance

• Provide technical oversight across multiple client engagements and ensure consistent delivery quality.
• Define and enforce engineering practices covering coding standards, code reviews, testing, CI/CD, documentation, and knowledge management.
• Conduct technical health checks and delivery audits to identify risks and improvement opportunities.
• Partner with Delivery Managers and Architects to resolve complex technical and delivery challenges.

Client Engagement & Pre-Sales Support

• Act as a trusted technical advisor to clients on data strategy, AI/ML adoption, modernisation, and platform selection.
• Support pre-sales activities including solutioning, estimations, proposals, client presentations, feasibility assessments, and ROI evaluation.
• Translate business requirements into scalable data and AI/ML architectures and implementation approaches.
• Help clients progress from traditional reporting towards predictive, AI-augmented, and generative AI-driven decision-making.

Build and scale teams across Data Engineering, ML Engineering, Data Science, MLOps, Architecture, and Technical Leadership.

Define skill matrices, learning paths, and certification plans across data and AI/ML technologies.

Mentor senior engineers and data scientists and develop future practice leaders and architects.

Talent Development & Capability Building Foster a culture of engineering excellence, responsible AI, ownership, innovation, and continuous learning.  

Requirements

Skills and Experience

• Core Experience

• 12–15 years of experience across AI, ML, Data Engineering, analytics platforms, or large-scale integration programmes.

• 5+ years in a senior technical leadership, architecture, or practice-building role.

• Proven experience building and scaling enterprise-grade data platforms and solutions.

• Experience working across multiple client engagements and managing competing priorities.

• Technical Expertise

• Strong hands-on and architectural experience with modern data and AI/ML platforms, including:

• Azure Data Factory and Microsoft Fabric or equivalent platforms.

• Azure Machine Learning, Databricks/MLflow, or equivalent ML platforms.

• Azure AI Foundry, Azure OpenAI, AI Agents and exposure to Agentic Architecture.

• ETL/ELT, streaming and event-driven data pipelines.

• Data lakehouse and warehouse architectures.

• Feature engineering, model training/serving and MLOps.

• GenAI concepts including LLMs, prompt engineering, RAG, MCP, vector databases, and agentic frameworks.

• AI productivity/tooling such as Claude, Microsoft Copilot, or similar.

• Experience across cloud platforms; Azure preferred, with AWS/SageMaker exposure being an advantage.

• DevOps, CI/CD and Infrastructure-as-Code for data and ML pipelines.

• Working knowledge of Responsible AI, including explainability, bias/fairness, governance, and AI adoption within the SDLC.

• Leadership & Business Skills

• Strong stakeholder management and executive communication skills.

• Ability to balance technical depth with business and commercial considerations.

• Strong strategic thinking with a bias towards execution and measurable outcomes.

• Ability to influence and lead across teams without relying solely on formal authority.

• Strong client-facing and consultative approach.

Personal Attributes

• Passionate about building a world-class, future-ready engineering practice.
• Technology-agnostic, curious, innovative, and forward-thinking.
• Strong ownership and accountability for outcomes.
• Entrepreneurial mindset with a focus on value creation, scalability, and continuous improvement.
• Passionate about developing people and building a culture of technical excellence.