As a Pod Lead, you will bridge high-level technical leadership, hands-on engineering execution, team leadership and consulting. You will lead, mentor, and grow a dedicated pod of AI Engineers while directly designing, deploying, and evaluating cutting-edge AI systems.
You will own end-to-end project delivery quality, account growth, and team performance while remaining a key technical architect and direct builder on production workloads.
1. Hands-On Engineering & AI Systems
- Design & Build AI Workloads: Design, deploy, and operate production-grade AI systems, including agentic workflows, Retrieval-Augmented Generation (RAG), orchestration, and foundation model fine-tuning.
- Rigorous Evaluation: Build evaluation frameworks, define success criteria, and measure system performance using offline metrics, online quality monitoring, human-in-the-loop reviews, and failure-mode analysis.
- Production Engineering: Build robust production pipelines, serving architectures, and monitoring for AI and ML workloads on client platforms (primarily GCP).
- Agentic Delivery: Use coding and research agents daily in delivery, and build custom agents for evaluation suites, prompt/tool regression, data labeling, and repeatable team workflows.
2. Team Leadership & Talent Development
- People Management: Lead, mentor, and grow a pod of MLEs and Data Scientists, taking ownership of hiring, career development, and performance management.
- Retention & Engagement: Manage pod utilization, identify risks early, drive team certifications, and cultivate a high-trust, inclusive, and innovative engineering culture.
- Technical Governance: Set and uphold technical standards, conduct architecture and code reviews, and define best-practice ways of working across the pod.
3. Client & Strategic Leadership
- Trusted Advisory: Build strong client relationships, translating business challenges into AI system designs with explicit constraints and measurable success criteria.
- Technical Communication: Articulate complex architectural and technical concepts clearly to both technical teams and senior client executive stakeholders.
- Cross-Functional Alignment: Collaborate with Software, Data, Platform, Analytics, and UX engineers to ensure systems are usable, governable, and operable.
4. Delivery & Commercial Impact
- Delivery Ownership: Own delivery quality, timelines, and scope discipline across pod engagements, surfacing scope creep as strategic expansion opportunities.
- Account Depth: Proactively identify growth opportunities within existing accounts to expand client relationships beyond the initial Statement of Work (SOW).
- Pre-Sales & Scoping: Serve as the subject matter expert in pre-sales, leading solution design, SOW scoping, and effort estimation for complex AI initiatives.
- Practice Initiatives: Contribute reusable assets, methodologies, evaluation suites, and public technical content to advance the broader AI practice.