datatonic - Team Lead, AI & Machine Learning
Requirements
• 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. • Commercial & Client Savvy: Experience in client-facing consulting or pre-sales environments, with comfort operating at the intersection of technical execution, delivery management, and account growth. • Communication: Exceptional verbal and written communication skills to convey complex technical paradigms to diverse stakeholders.
Responsibilities
• 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.
Benefits
• We believe in empowering our team to thrive, with benefits including: • Holiday: 25 days plus bank holidays (obviously!) • Health Perks: Private health insurance (Vitality Health) and Smart Health Services • Fitness & Wellbeing: 50% gym membership discounts (Nuffield Health, Virgin Active, Pure Gym). • Hybrid Model: A WFH allowance to keep you comfortable. • Learning & Growth: Access to platforms like Udemy to fuel your curiosity. • Pension: (Auto-enrollment after probation period. 3% employer contributions raising 1% per year of service to a max of 10%) • Life Insurance: (3 x your base salary!) • Income Protection: (up to 75% of base salary, up to 2 years) • Cycle to Work Scheme • Are you ready to make an impact?
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