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AI Architect
·Israel
Not specifiedFull-timeAI Engineeringhealthcare
Director-level AI Architect to provide system-level architectural guidance, lead hands-on prototyping of AI innovations, establish best practices, and ensure compliance for a digital health platform. Requires deep expertise in machine learning, deep learning, LLMs, and agentic systems.
Responsibilities
- Guide AI architectural direction across the platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
- Act as a senior technical reviewer and thought partner for complex AI design decisions.
- Provide technical oversight on exploring new models, technologies, and opportunities.
- Surface architectural risks, tradeoffs, and long-term implications, including cost, security, and compliance.
- Spend at least 50% of time hands-on building end-to-end AI prototypes, technical demos, and proofs of concept.
- Drive applied innovation that de-risks new technologies, demonstrates feasibility, and informs product direction.
- Transition successful prototypes to team ownership for further development and scaling.
- Define and promote best practices for applied AI development, including rapid prototyping, agent design, orchestration, experimentation, and validation.
- Help teams align on shared technical patterns, tools, and standards.
- Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
- Lead technical deep dives, architecture discussions, and design reviews.
- Ensure AI development practices align with applicable regulations for handling sensitive medical data.
- Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation.
- Support and contribute to AI-related compliance and regulatory documentation in collaboration with Legal, Security, and Medical Research teams.
- Serve as a technical point of reference for AI compliance questions.
Requirements
- Proven experience designing and building complex AI systems delivered to production, with end-to-end understanding of research, architecture, validation, and production handoff.
- Strong hands-on experience with modern AI approaches, including Machine Learning, Deep Learning, and LLM-based systems; experience with agentic AI systems or orchestration patterns is a strong advantage.
- Demonstrated ability to move quickly from idea to working prototype, with a strong passion for hands-on experimentation and applied innovation.
- Experience working in environments involving sensitive data and regulatory constraints, with understanding of how these considerations shape AI system design.
- Excellent system-level technical judgment, including ability to identify risks, tradeoffs, and unintended consequences.
- Proven ability to act as a technical leader without formal authority, influencing and guiding senior peers through collaboration and expertise.
- Strong communication and interpersonal skills, with ability to explain complex technical concepts to diverse stakeholders.
- Ability to contribute to clear technical and AI-related compliance documentation.
- High proficiency in Python and modern AI/ML tooling.
Nice to have
- Deep experience in NLP, NLU, or clinical text processing.
- Experience deploying LLMs or agent-based systems in production.
- Familiarity with cloud-native ML stacks (AWS, Docker, Kubernetes).