This role pays more than
72%
of similar roles. Most pay
$187,850–$262,843
— the shaded band above.
At the midpoint, this role pays about
$258k
versus about
$225k
for comparable roles.
Based on 239 similar postings.
Employer
About Abbott
Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices
Abbott currently has
90 open roles
on FindRole.
Listed pay typically runs
$99,300–$198,700
across 89 roles with salary data.
As the Head of AI and Data Platform for the Lingo division, you will define the technical vision and lead the engineering execution of AI, machine learning, and data infrastructure. You will be a hands-on builder responsible for architecting biosensor data ingestion pipelines, feature stores, model serving layers, and data lake designs. Your daily work involves developing LLM-based conversational health experiences, RAG pipeline architectures, and MLOps infrastructure including experiment tracking and automated retraining. You will manage the technical lifecycle of models to ensure reliability in a regulated environment while overseeing engineering standards for prompt engineering and safety guardrails. The role requires expertise in distributed data systems, real-time streaming, and cloud platforms like Azure, AWS, or GCP. You will solve complex problems involving high-volume biosensor data to provide personalized metabolic health insights and predictive analytics.
Define and own the technical architecture for AI and data platforms, including ingestion pipelines, feature stores, and data lake designs.
Drive the engineering execution of AI-powered features like personalized health insights and LLM-based conversational experiences.
Establish engineering standards for ML systems, including RAG architectures, prompt engineering, and evaluation frameworks.
Build and manage MLOps infrastructure for experiment tracking, model registries, automated retraining, and production monitoring.
Translate data science research and product requirements into scalable, production-ready AI systems at consumer scale.
Ensure all AI and ML systems comply with regulatory requirements such as HIPAA, GDPR, and FDA guidelines.
Standardize tools, development processes, and data engineering practices across multiple AI and data squads.
Define and report on engineering OKRs, KPIs, and delivery metrics to senior stakeholders.
What we're looking for
Bachelor's degree in computer science, Engineering, Mathematics, or a related technical discipline.
Advanced degree in Machine Learning or Data Science is preferred.
15+ years of progressive experience in software and data engineering.
Proven hands-on experience architecting large-scale data platforms including real-time streaming pipelines and ML serving infrastructure on cloud platforms.
Deep system design expertise in distributed data systems and event-driven data architectures.
Hands-on experience engineering AI products, including LLM integration, RAG pipelines, and deploying ML models in consumer applications.
Strong understanding of AI/ML system design considerations such as model serving, latency, cost trade-offs, and safety constraints.
Exceptional executive communication skills to translate technical complexity into business narratives for senior leadership.
Data Architecture
AI Architecture
GenAI
MLOps
LLMOps
RAG
Agentic AI
MDM
Data Governance
Cloud-Native
AWS
Azure
Google Cloud
Data Mesh
Data Modeling
Semantic Layer
Feature Stores
TOGAF