The Staff Data & AI Architect will define and own the end-to-end architecture of our companys data and AI platform, spanning large-scale data systems, ML experimentation infrastructure, and production-grade model deployment. This role sets the architectural vision and technical standards for data and AI infrastructure across the company, ensuring scalable, compliant, research-accelerating systems that power both research and product development in a regulated biomedical environment.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Architect and evolve a scalable, resilient, and secure data and AI infrastructure supporting our companys research and product platforms. Identify recurring infrastructure and workflow challenges to drive the creation of generalized, reusable platform solutions.
Define scalable experimentation and MLOps patterns supporting experiment tracking, compute orchestration, dataset versioning, and reproducibility guarantees.
Lead the design and implementation of enterprise-grade data management. Establish and enforce data principles (e.g., FAIR, Medallion/Lakehouse architectures) to ensure data quality, lineage, discoverability, and reuse, while guaranteeing that all infrastructure meets security, privacy, and regulatory requirements.
Serve as the senior technical authority for data and AI infrastructure, partnering with Research, Engineering, and Product leadership to align architectural decisions with scientific and business priorities.
Lead technology evaluation and adoption decisions for AI infrastructure, balancing scalability, cost efficiency, compliance, and long-term maintainability.
Requirements: 8+ years of experience building and operating large-scale data and AI systems, with 5+ years in architecture or platform leadership roles driving technical direction across multiple teams.
Demonstrated experience defining and executing architectural vision in complex, multi-team environments, including setting standards, leading design reviews, and influencing long-term roadmaps.
Deep expertise in cloud-native distributed systems (GCP, AWS, or Azure), including Kubernetes-based infrastructure (e.g., GKE), scalable storage and compute, reliability, and security at scale.
Strong command of modern data architectures (data lake, lakehouse, warehouse) and large-scale analytics platforms (e.g., BigQuery, Databricks), including data lifecycle, metadata, and governance strategy.
Proven experience designing internal data and ML platforms that enable reproducible experimentation, self-serve workflows, and efficient research-to-production transitions.
Experience architecting end-to-end ML lifecycle systems, including training infrastructure, experiment tracking, versioning, CI/CD for ML, deployment patterns (batch/online), and monitoring across MLOps platforms (e.g., ClearML, RunAI, or equivalents).
Strong understanding of security, privacy, and compliance requirements in regulated environments, including fine-grained access control, auditing, and data protection best practices.
Desired personal traits:
You want to make an impact on humankind
You prioritize We over I
You enjoy getting things done and striving for excellence
You collaborate effectively with people of diverse backgrounds and cultures
You have a growth mindset
You are candid, authentic, and transparent.
This position is open to all candidates.