Role summary
Lead H3 Zoom's AI and data engineering function to build, deploy, and continuously improve production-grade computer vision and machine-learning systems. You will own the end-to-end AI platform (data pipelines, model training/inference, MLOps, and model governance), while coaching a high-performing team and partnering closely with Product, Field Operations, and Customer Delivery to deliver measurable customer outcomes.
Key responsibilities
- Team leadership: hire, mentor, and lead AI, data science, and data engineering team members; set clear performance standards and growth plans.
- Technical strategy: translate the AI roadmap into an execution plan, architecture, and quarterly delivery milestones (SaaS, managed services, and partner deployments).
- Data engineering: design and operate scalable ingestion, labeling, storage, and retrieval pipelines for imagery/video and associated metadata; ensure dataset versioning and provenance.
- MLOps and model lifecycle: implement repeatable training and evaluation pipelines, model registry, CI/CD for ML, monitoring (performance, drift, latency), and safe rollback.
- Microservices and infrastructure: evolve AI microservices and APIs; optimize for reliability, throughput, and cost in cloud environments.
- Model development and evaluation: lead experimentation and selection of algorithms/models; apply sound statistical methods (confidence intervals, sampling, bias/error analysis) to validate performance.
- Responsible data use and governance: establish best-practice controls for data access, privacy, security, and compliance; define model risk controls and auditability.
- Cross-functional delivery: work with Product, QA/QC, and delivery teams to define requirements, acceptance criteria, and release readiness; support customer escalations and root-cause analysis.
- Knowledge-sharing: create internal standards (coding, data, MLOps), documentation, and playbooks; uplift engineering rigor across the team.
Required experience and qualifications
- Bachelor's degree in Computer Science, Software Engineering, Data Science, Statistics, or related field; postgraduate qualifications in ML/CV/Statistics are a plus.
- 5+ years of hands-on experience delivering applied ML, computer vision, and data engineering systems; proven track record shipping models to production.
- Prior experience recruiting, building, and managing an AI/data team (technical leadership plus people leadership).
- Strong software engineering fundamentals (Python; API/microservices design; testing; code review).
- Practical MLOps experience (training pipelines, experiment tracking, model registry, deployment, monitoring).
- Comfort with cloud-native engineering (containers, orchestration, infrastructure-as-code concepts) and data platforms (SQL/NoSQL, object storage, data lakes/warehouses).
- Strong statistics/probability foundation to evaluate models and data quality, and to communicate uncertainty clearly.
Preferred / nice-to-have
- Domain experience in the built environment, facilities management, civil/structural engineering, or inspection workflows.
- Experience with large-scale image/video analytics, 3D reconstruction/photogrammetry, or multi-modal datasets.
- Experience defining data governance frameworks and working with security/compliance requirements (e.g., ISO 27001 controls, access management, audit trails).
- Experience integrating AI services with enterprise systems (CMMS/EAM, project management platforms) and supporting multi-tenant SaaS deployments.
Key competencies
- Execution leadership: balances research ambition with delivery discipline; prioritizes what moves customer and business outcomes.
- Architecture and quality mindset: designs for reliability, maintainability, and observability; raises engineering standards.
- Clear communication: translates technical trade-offs into stakeholder-ready decisions; writes strong documentation.
- Ownership: takes end-to-end responsibility for outcomes, from data to deployment and monitoring.
- Collaboration: works effectively across Product, Field Ops, and Customer Delivery in fast-moving environments.
Why Join Us
- Unlimited leave policy, based on trust and accountability
- Hybrid work arrangement, balancing flexibility and collaboration
- Health and dental coverage for employees
- Opportunity to work in a startup environment with a highly collaborative and open culture
- Hands-on exposure to real-world datasets and operational workflows, enabling you to design, build, and deploy AI solutions that drive measurable business impact.
