VP AI Engineering

Florida Blue

About The Position

We are seeking a bold, technically deep, and strategically minded engineering executive to lead our AI Engineering organization. As VP of AI Engineering, you will own the architecture, development, and operation of the platforms and systems that power AI-driven innovation across our health plan enterprise. This is a critical leadership role responsible for transforming how our organization leverages artificial intelligence from predictive models that improve member outcomes to automation that drives operational efficiency across claims, care management, and utilization review. You will partner closely with the VP Of AI Factory to execute prioritized AI initiatives and ensure it meets the specification laid out by that organization. You will serve as the senior engineering authority for all AI initiatives, working in close partnership with AI Factory leader, Product, AI Governance, Data Science, Clinical Operations, and executive leadership to deliver production-grade AI systems that are scalable, compliant, and clinically sound. You will be accountable for engineering excellence, team growth, and measurable business impact.

Requirements

Bachelor's degree of additional equivalent work experience
10+ years in software or AI/ML engineering, with 5+ years leading multi-team engineering organizations at the senior director or VP level
Deep understanding of the healthcare payer landscape, including health plan operations, claims processing, utilization management, and care management programs
5+ years' hands-on experience building and deploying machine learning or AI systems in production, preferably within a healthcare or regulated industry environment
Deep proficiency in Python; working knowledge of SQL, Java, or Scala a plus.
Experience with PyTorch, TensorFlow, Hugging Face and LangChain or similar LLM orchestration frameworks
Direct experience building and deploying large language models, including fine-tuning, RAG architectures, prompt engineering, and safety/evaluation frameworks in regulated environments
MLOps & Platform Engineering: Strong hands-on experience with MLflow, Kubeflow, Airflow, or equivalent tools for orchestration, experiment tracking, and model lifecycle management.
Deep expertise with AWS, Azure, or GCP, including managed AI/ML services (e.g., SageMaker, Azure ML, Vertex AI)
Familiarity with HL7, FHIR, ICD-10, CPT, and claims data structures; experience working with EHR data, ADT feeds, or clinical NLP a strong plus
Experience with modern data stack components including data lakes, streaming pipelines (Kafka, Flink), and vector databases
Proven track record of building and scaling high-performing engineering teams, including hiring, mentoring, and developing senior and staff-level engineers.
Exceptional ability to translate complex engineering and AI concepts for non-technical executive, clinical, and regulatory audiences
Demonstrated ability to drive large-scale technical programs from strategy through delivery in complex, matrixed healthcare organizations

Nice To Haves

M.S. or Ph.D. in Computer Science, Software Engineering, Biomedical Informatics, or a related field

Responsibilities

In collaboration with the office of the CEO and VP Of AI Factory, execute on the multi-year technical roadmap for AI engineering, aligned with the company's strategic goals across care delivery, cost management, and member experience.
Serve as the senior engineering voice for AI at the executive level, partnering with VP Of AI Factory, influencing platform investment, architecture direction, and build-vs-buy decisions across the enterprise.
Partner with the CTO, Chief Data Officer, VP Of AI Factory and clinical leadership to ensure AI engineering capabilities support the full AI lifecycle from research and experimentation to scalable production deployment.
Architect and operate a cloud-native, enterprise-grade AI platform that supports model training, evaluation, versioning, deployment, and monitoring at scale.
Establish and enforce MLOps best practices including CI/CD pipelines for model development, automated testing, model registry management, and drift detection.
Drive infrastructure strategy across compute, orchestration (e.g., Kubernetes, Airflow), and data pipelines to optimize cost, performance, and regulatory compliance.
Lead engineering teams responsible for developing, fine-tuning, and deploying machine learning models and LLM-powered solutions into production healthcare environments.
Oversee integration of AI models with core health plan systems including claims platforms, EHRs, care management tools, and member-facing applications ensuring high availability, low latency, and auditability.
Champion rigorous model evaluation, A/B testing, and continuous improvement frameworks appropriate for high-stakes healthcare use cases.
Recruit, develop, and retain a world-class team of AI engineers, ML engineers, and data scientist with deep healthcare domain exposure.
Build an engineering culture rooted in technical ownership, clinical accountability, psychological safety, and continuous learning.
Define career frameworks, leveling guides, and growth paths for the AI engineering organization.
Establish and enforce AI engineering standards covering responsible AI, bias detection, model explainability, and clinical safety in alignment with HIPAA, CMS regulations, and applicable state requirements.
Ensure all AI systems and infrastructure meet the company's security, data privacy, and compliance standards, protecting sensitive member and clinical data.
Partner with Office of CEO, Legal, Compliance, Security, and Risk teams to identify, assess, and mitigate technical and ethical risks associated with AI deployments, including third-party and vendor AI solutions.
Develop and maintain AI governance policies and audit frameworks to support regulatory reporting and internal oversight.
Collaborate with Office of CEO, clinical, operational, and business stakeholders to translate complex engineering capabilities into clear, actionable AI solutions.
Communicate AI engineering strategy, platform performance, and risk posture to executive leadership and board-level stakeholders with clarity and confidence.
Work closely with vendor and technology partners to evaluate and integrate external AI capabilities into the enterprise platform.
Define and own engineering KPIs including model performance, system reliability (SLOs/SLAs), deployment velocity, infrastructure cost efficiency, and clinical outcome metrics.
Provide regular executive reporting on AI engineering progress, translating technical performance into measurable business and clinical impact.
Lead cost optimization efforts across AI infrastructure without compromising platform capability or compliance posture.

Benefits

Medical, dental, vision, life and global travel health insurance
Income protection benefits: life insurance, short- and long-term disability programs
Leave programs to support personal circumstances
Retirement Savings Plan including employer match
Paid time off, volunteer time off, 10 holidays and 2 well-being days
Additional voluntary benefits available
A comprehensive wellness program

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume