Practice Lead

Role Overview:

The AI Development Practice Lead is a technically credible, forward-leaning individual who has spent meaningful time building with AI coding tools and is now ready to shape how an enterprise engineering organization adopts, scales, and governs them. This is not a pure architecture role yet — it requires someone still close to the tools, the code, and the engineers. The person in this seat will define patterns, build reusable systems, influence tooling strategy, and present outcomes at SVP level. For the right candidate, this role is the launchpad into an AI Architect trajectory.

Key Responsibilities:

AI-Assisted Development & Tooling

• Serve as the deepest hands-on practitioner of AI coding tools — Claude Code and GitHub Copilot — within the data engineering organization, setting the standard for how these tools are used effectively.
• Go beyond power-user proficiency: build Skills artifacts, configure MCP servers, design prompts, and evaluate new models before broader rollout.
• Identify where AI tooling creates the most leverage in data engineering workflows and systematically drive adoption in those areas first.
• Stay ahead of the AI coding tool landscape, continuously evaluating new models and capabilities via AWS Bedrock and other platforms.

Skills Artifacts & Institutional Knowledge Architecture

• Design and maintain a library of markdown-based Skills artifacts that encode organizational knowledge — EDF lookups, DAT/DRD workflows, pipeline patterns — making AI tools contextually smarter within our specific environment.
• Establish standards and governance for how Skills artifacts are structured, versioned, and maintained as a living knowledge system.
• Work with senior data engineers and domain leads to extract tacit institutional knowledge and systematize it into reusable AI-consumable form.

Adoption, Measurement & Behaviour Change

• Drive AI tool adoption across 100+ data engineers through targeted enablement, workflow integration, and community building — not classroom training.
• Design and maintain adoption scorecards and dashboards using the GitHub Telemetry API to track meaningful signals: code acceptance rates, usage depth, and productivity trends.
• Present adoption progress, model evaluation outcomes, and strategic recommendations directly to SVP-level stakeholders.

Architecture Influence & Forward Planning

• Contribute to the emerging AI tooling architecture — MCP server configuration, RAG pipeline design, prompt engineering standards, and model selection frameworks.
• Partner with platform engineering, cloud, and AI/ML teams to ensure AI coding tooling integrates cleanly with the broader data and cloud architecture.
• Build the internal point of view on where AI-assisted development is heading and what the organization needs to invest in next.

Required Skills and Experience:

• 7+ Years Experience in AI including Generative AI, Classic AI/ML, NLP, Data Analysis etc.
• Experience in Python based ML Modelling, data preprocessing, Agentic orchestration etc.
• Has built Skills artifacts, configured MCP servers, and evaluated AI models — demonstrably beyond casual tool usage.
• Working knowledge of RAG architecture, prompt design, and the practical mechanics of how LLMs behave in development contexts.
• Familiarity with AWS Bedrock, AWS Lambda, and Claude model in production.
• Experience designing telemetry-based adoption dashboards using the GitHub Telemetry API or equivalent.
• Aspiring toward an AI Architect career trajectory — intellectually curious about model behavior, system design, and the evolving AI development landscape.
• Self-directed; comfortable operating without daily management in ambiguous, high-visibility environments.
• Credible and articulate when presenting AI work and business impact to senior business stakeholders.
• Based in the US; able to work on-site.

Good to Have:

• Experience with Azure and Databricks, including production notebooks and data pipeline development.
• 2+ years of deep, hands-on experience with AI coding tools — Claude Code and GitHub Copilot experience is required.
• Awareness of SAP data models, including ECC and S/4 HANA structures.
• Hands-on experience with LLM performance evaluation and hallucination mitigation strategies.
• Familiarity with observability and monitoring frameworks for AI systems in production.
• Domain background in CPG or supply chain data environments.
• Experience in an embedded client model rather than a delivery center structure.
• Early exposure to formal AI architecture work — solution design, reference architectures, or AI governance frameworks.

Work Arrangement:

This role is based in the US -Dallas and requires the ability to work on-site. Candidates should be within commutable distance of one of our hub locations or open to regular on-site presence at client locations