Director of AI and Data Engineering

AI and Data Strategy & Architecture

• Define and execute a comprehensive strategy for AI and data infrastructure, focused on scalability, reliability, and business impact.
• Drive innovation in machine learning (ML) and large language model (LLM)-powered applications, from concept to production.
• Evaluate and implement emerging AI techniques (e.g., retrieval-augmented generation, agentic workflows, multimodal models) aligned to our diagnostics and consumer product roadmap.

Technical Leadership:

• Lead architectural decisions across data warehousing, pipelines, model deployment, and real-time inference systems.
• Design and evolve our data ecosystem (including structured lab/test data, behavioral data, partner integrations) to serve both AI initiatives and enterprise analytics needs.
• Oversee development and integration of ML models into customer-facing features, health recommendations, and operational tooling.

Team Management and Enablement:

• Manage and mentor a high-performance team of data engineers and AI/ML specialists, including Principal-level technical contributors.
• Act as a “player-coach”—deeply technical and capable of guiding architecture while fostering team execution and growth.
• Establish scalable practices for MLOps, experimentation frameworks, and model performance tracking.

Cross-Functional and Business Impact:

• Partner with Product, Clinical, Engineering, and Commercial teams to identify and prioritize AI use cases that align with core business goals.
• Translate data and AI capabilities into clear, compelling value propositions for internal and external stakeholders.
• Ensure systems and models comply with HIPAA and other regulatory requirements while maintaining a strong security and data governance posture.

Who You Are:

• 10+ years of experience in data engineering, AI/ML development, or related fields, with 3+ years in a leadership role.
• Proven track record of designing and deploying ML or AI systems in production environments, particularly in healthtech or regulated industries.
• Strong experience with Cloud-native data architectures (AWS/GCP/Azure), Machine learning pipelines and model lifecycle (e.g., Vertex AI, SageMaker, MLFlow), Data processing frameworks (Spark, Airflow, DBT, etc.), and Modern LLM stacks (e.g., LangChain, transformers, RAG pipelines, fine-tuning workflows)
• Deep understanding of enterprise data management, data governance, and secure system design.
• Strategic mindset: able to balance short-term delivery with long-term platform vision.
• Excellent communicator and cross-functional collaborator—able to align diverse teams around shared AI and data goals.

Preferred QualificationsL

• Expertise in applied artificial intelligence.
• Hands-on experience with machine learning (supervised, unsupervised, and reinforcement learning).
• Deep familiarity with LLM architectures (e.g., GPT, BERT, LLaMA) and generative AI use cases, including RAG pipelines, fine-tuning, prompt engineering, and evaluation frameworks.
• Experience implementing AI-powered features in production environments.
• Significant experience architecting modern data ecosystems, including Scalable data pipelines and warehousing (e.g., Snowflake, BigQuery, Redshift), data modeling, quality frameworks, and governance standards and Integrating structured and unstructured data for use in advanced analytics and AI.
• Experience with healthcare-specific data (e.g., lab test results, claims data, HL7/FHIR integration).
• Familiarity with HIPAA, HITRUST, and other healthcare data privacy and security compliance frameworks.
• Background in a consumer health or diagnostics startup, scale-up, or growth-stage environment where velocity, quality, and innovation must co-exist.