Head of AI and Data Platform
Role details
Job location
Tech stack
Job description
We are seeking an experienced and hands-on Head of AI and Data Platform to define the technical vision and lead the engineering execution of Lingo's AI, machine learning, and data infrastructure. This is a leadership role for a builder who has personally architected and delivered production AI and data systems at scale, brings deep technical instincts across ML engineering, data platform design, and LLM-based product development, and knows how to grow and galvanize engineering teams to ship innovative AI-powered health products in a regulated, fast-moving environment.
This is not a strategy-only role. You will be deeply involved in system design, architecture decisions, and critical technical problem solving as a contributor, not just a reviewer, while also building and leading a high-performing global team of AI and data engineers.
What You'll Work On
- Define and own the technical architecture for Lingo's AI and data platform, including biosensor data ingestion pipelines, real-time and batch processing infrastructure, feature stores, model serving layers, and data lake design.
- Drive the architecture and engineering execution of AI-powered product features, including personalized metabolic health insights, predictive analytics from CGM data, LLM-based conversational health experiences, and on-device ML inference from biosensor data.
- Establish engineering standards for AI system design, including model integration patterns, RAG pipeline architecture, prompt engineering practices, evaluation and observability frameworks, and responsible AI guardrails appropriate for a regulated health context.
- Lead the engineering execution of ML model development, training, evaluation, and deployment pipelines, ensuring models reach production reliably, safely, and with the observability required to detect drift and degradation.
- Build and own MLOps infrastructure including experiment tracking, model registry, automated retraining pipelines, A/B testing frameworks, and model monitoring for production AI systems.
- Partner with Data Science and Product teams to translate model research and product requirements into scalable, production-ready AI systems that perform reliably at consumer scale.
- Define standards for LLM integration, including prompt management, retrieval-augmented generation, evaluation harnesses, latency and cost optimization, and safety guardrails for health-related conversational AI.
- Ensure AI and ML systems maintain ongoing alignment with regulatory requirements including HIPAA, GDPR, and FDA software guidance, in close partnership with Regulatory Affairs, Legal, and Quality Assurance.
- Define, manage, and report on engineering OKRs, KPIs, and delivery metrics for the AI and Data Platform function, presenting progress and insights to senior stakeholders.
- Standardize tools, development processes, and data engineering practices across AI and data squads to improve alignment, data quality, and delivery consistency.
Requirements
- Bachelor's degree in computer science, Engineering, Mathematics, or a related technical discipline. Advanced degree in Machine Learning, Data Science, or equivalent preferred.
- 15+ years of progressive experience in software and data engineering, with a strong foundation as an individual contributor who has personally built production AI and data systems at scale before moving into leadership.
- Proven hands-on experience architecting and building large-scale data platforms, including real-time streaming pipelines, data lakes, feature stores, and ML serving infrastructure on cloud platforms (Azure, AWS, or GCP).
- Deep system design expertise in distributed data systems: you can whiteboard and lead the design of event-driven data architectures, data models, caching strategies, and real-time biosensor data pipelines from first principles.
- Meaningful hands-on experience engineering AI-powered products, including integrating LLMs into production systems, building RAG or agentic pipelines, and deploying ML models within consumer-facing applications.
- Strong understanding of AI and ML system design considerations, including model serving infrastructure, latency and cost trade-offs, evaluation frameworks, observability, data feedback loops, and safety constraints in sensitive health domains.
- Exceptional executive communication skills, including the ability to translate AI and data complexity into clear product and business narratives for C-suite and board audiences., * Background in digital health, consumer health technology, or other highly regulated industries such as HIPAA, GDPR, and FDA oversight.
- Hands-on experience with IoT or biosensor data ingestion pipelines, real-time analytics, or wearable device platforms.
- Experience applying AI and ML in health or wellness contexts, including personalization engines, anomaly detection on sensor data, or clinically informed recommendation systems.
- Experience with IEC 62304-based software development processes for medical device software.
- Experience in high-growth scale-up or venture-backed environments with exposure to rapid product and organizational scaling.
- Strong experience defining OKRs, KPIs, and capacity planning for high-throughput, consumer-facing AI platforms with high-availability requirements.
Benefits & conditions
Learn more about our health and wellness benefits, which provide the security to help you and your family live full lives: www.abbottbenefits.com
Follow your career aspirations to Abbott for diverse opportunities with a company that can help you build your future and live your best life. Abbott is an Equal Opportunity Employer, committed to employee diversity.
The base pay for this position is $172,000.00 - $344,000.00