Shape the Future of AI & Data with Us

Datatonic is Google Cloud's premier AI and data partner, specializing in designing, building, and operating production AI systems. We combine software engineering, machine learning, data engineering, and analytics on Google Cloud Platform (GCP) with top-tier consulting skills to turn complex business requirements into reliable, production-grade products.

Your Mission

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.

What You’ll Do

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.

What You’ll Bring

Experience & Skills

Proven Track Record: 7+ years of experience in machine learning, data science, or applied AI, with at least 2+ years leading technical teams or projects end-to-end.

Hands-On & Architectural Expertise: Strong software engineering foundations across the modern AI stack:

Classical ML & Stats: Classical ML techniques (TensorFlow, PyTorch, scikit-learn), forecasting, and feature engineering.

Generative AI & Agents: RAG, tool-calling, orchestration, guardrails, LLM-based agentic architectures, and fine-tuning.

Cloud & MLOps: Production deployment on cloud platforms (Google Cloud preferred: Vertex AI, BigQuery ML, Gemini).

Evaluation Mindset: A track record of moving beyond exploratory notebooks to define, measure, and prove system quality with evidence and metrics.