Director, AI Engineering

Role Overview

• Define and execute the AI engineering strategy and technical roadmap, ensuring alignment with Lantern’s product, clinical, and business priorities.
• Build, lead, and scale a high-performing AI engineering team, including hiring, mentoring, performance management, and creating a culture of technical excellence and innovation.
• Drive rapid AI proof-of-concept development, moving from problem identification to working prototypes on aggressive timelines, and shepherding successful POCs into production-grade systems.
• Own the end-to-end lifecycle of ML/LLM systems—from research and experimentation through production deployment, monitoring, and continuous improvement.
• Establish and maintain best-in-class MLOps practices: experiment tracking, reproducibility, CI/CD for models, automated testing, drift detection, observability, and scalable infrastructure on Azure.
• Partner with product, clinical operations, marketing, data, and engineering leadership to identify high-impact AI opportunities and translate them into well-scoped, executable initiatives.
• Lead architecture decisions for ML/LLM systems, ensuring scalability, reliability, security, and compliance with healthcare regulations.
• Establish clear KPIs and success metrics for the AI engineering function, including model performance, delivery velocity, team health, and business impact.
• Champion responsible AI practices across the organization, including fairness, transparency, explainability, and regulatory compliance in healthcare contexts.
• Represent the AI engineering function to executive leadership, providing regular updates on progress, risks, and strategic recommendations.
• Build external visibility for Lantern’s AI capabilities through thought leadership, community engagement, and talent brand building.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience; Master’s preferred.
• 10+ years of experience in software engineering or machine learning, with at least 4 years in a leadership role managing ML/AI teams.
• Proven track record of building and shipping production ML/LLM systems at scale, including end-to-end ownership from research through deployment.
• Deep technical expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), LLM technologies (prompt engineering, RAG, embeddings, fine-tuning, agents), and modern MLOps practices.
• Strong experience with cloud-native ML infrastructure on Azure, including Azure ML, Databricks, and Azure DevOps pipelines.
• Demonstrated ability to rapidly prototype AI solutions and drive them to production on aggressive timelines.
• Experience with big data ecosystems (Spark, Databricks, Delta Lake) and streaming technologies.
• Excellent leadership skills: hiring and developing high-performing teams, managing cross-functional relationships, and influencing at the executive level.
• Strong strategic thinking with the ability to balance long-term vision with near-term execution and business impact.
• Experience operating in regulated environments, ideally healthcare, with an understanding of compliance, data governance, and responsible AI principles.
• Outstanding communication skills with the ability to translate complex technical concepts for non-technical stakeholders and executive audiences.

Tech Stack

• Azure
• Cloud
• PyTorch
• Scikit-Learn
• Spark
• Tensorflow

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave