This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make data and AI platforms scalable, reproducible, secure, compliant, and easier to use.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Architectural Leadership: Own and evolve the technical roadmap for data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.
8+ years of industry experience in infrastructure, platform, data, or ML engineering, with a deep background in designing production infrastructure for data-intensive or AI/ML systems.
Hands-on expertise building and operating MLOps systems (for model development, training, and deployment) and managing GPU infrastructure, including scheduling, resource management, and utilization.
Proficient in managing data infrastructure technologies (e.g., BigQuery, data warehouses, object storage, orchestration systems like Dagster or Airflow) and operating within Kubernetes/containerized environments.
Demonstrated ability as a player-coach, including people-management experience or leading small engineering teams, with a focus on mentoring senior engineers and influencing technical direction.
Strong communication skills with the ability to partner effectively across diverse groups, including Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics and Leadership.
