Lead AI Engineer

St. Jude Children's Research Hospital

7d•Remote

About The Position

St. Jude is where those with a passion for making a difference come to break new ground. Located in Memphis, Tennessee, the mission of St. Jude Children’s Research Hospital is to advance cures, and means of prevention, for pediatric catastrophic diseases through research and treatment. We are leading the way the world understands, treats and defeats childhood cancer and other life-threatening diseases. We are looking for a Lead AI Engineer to join our Analytics Services team in supporting this incredible work. The Lead AI Engineer will play a pivotal role in advancing healthcare artificial intelligence (AI) initiatives by developing and training AI tools aimed at automating and optimizing clinical workflows, operational efficiencies, and administrative tasks. As part of our dynamic team, your expertise in AI and other cognitive computing methodologies, such as but not limited to machine learning (ML), large language models (LLMs), small language models (SLMs), natural language processing (NLP), Generative AI, and Agentic AI, will help revolutionize pediatric healthcare. This is your opportunity to designing, building, deploying, and operating production‑grade AI systems that automate and optimize clinical, operational, and administrative workflows across the institution. A day in the life of the Lead AI Engineer includes working with institutional stakeholders to deploy and integrate sophisticated AI systems, rigorously testing, validating, and tracking learning models, and troubleshooting issues to ensure system accuracy and reliability. A core objective of this role is to implement AI solutions that are reliable, explainable, auditable, and safe to operate in real‑world clinical and operational environments, with clear performance metrics, monitoring, and human‑in‑the-loop controls. These solutions should enable resource optimization and enhance decision-making, ultimately to support driving fulfillment of the St. Jude mission. As a leader, you will collaborate with analytics, data scientists, IT, informatics, and business stakeholders to ensure AI systems are scalable, secure, and seamlessly integrated into existing workflows. This position may be eligible for the possibility of remote work.

Requirements

Master’s degree in Computer Science, Computer Engineering, Data Science, Information Technology, or related field required.
Minimum Requirement: 5 years in designing high level architectures and developing solutions for large scale AI/ML systems and/or building solutions for a product on AI/ML features and capabilities.
Highly prefer 5+ years of experience designing architectures and building production-grade AI/ML systems (not just prototypes), including deployment, monitoring, and ongoing operations in a regulated or high-availability environment.
5+ years experience developing and deploying AI solutions using modern techniques (e.g., ML, NLP, LLM-based systems), with demonstrated ability to integrate solutions into enterprise workflows and platforms.
Proven experience with MLOps / LLMOps practices: CI/CD for models/prompts, model/version management, evaluation, drift monitoring, observability, incident response, and rollback strategies highly preferred.
Experience implementing monitoring, logging, and alerting for AI systems (quality, latency, cost, safety signals), and driving reliability improvements over time highly preferred.
Previous experience in process/workflow analysis and implementing AI solutions that measurably improve operational performance highly preferred.
Proficiency in AI technologies, machine learning, data analytics, and project management tools highly preferred.
Strong technical acumen to translate needs into engineering requirements, including APIs, integration patterns, infrastructure dependencies, and operational runbooks highly preferred.
Strong background building and deploying ML systems using supervised and unsupervised approaches OR LLM-based approaches, including evaluation and safety considerations highly preferred.
Strong background in industry use cases built on deep learning and machine learning (unsupervised and supervised techniques) is a must.
Deep learning frameworks such as TensorFlow, Keras, PyTorch, Time series analysis, anomaly detection, forecasting, predictive modeling, graph- based neural networks, Bayesian statistics, and text analytics are a must.
Expertise in Python and modern ML/AI frameworks (e.g., PyTorch or TensorFlow).
Experience with MLOps or similar tools for managing the lifecycle of machine learning models in healthcare applications.
Experience deploying AI systems in cloud or enterprise environments with attention to security, privacy, and compliance expectations.
Experience working with Epic electronic health records, including Cogito and Nebula.
Advanced knowledge in Machine Learning models and Natural Language Processing (NLP) techniques for healthcare data, including word embeddings and named entity recognition.
Implementing and fine-tuning Large Language Models (LLMs) using vector bases and Retrieval-Augmented Generation (RAG) for healthcare insights.
Ability to validate solution architectures for LLMs within healthcare systems, ensuring scalability and reliability.
Experience in deploying Generative AI (GenAI) models in production environments and providing ongoing support .
Familiarity with cloud platforms like Azure for deploying and scaling healthcare AI models.
Writing clean, efficient, and reusable code for healthcare machine learning applications.
Strong analytical mindset to analyze complex healthcare data, derive actionable insights, and solve problems effectively.
Proven ability to collaborate effectively with cross-functional teams, explain complex concepts to non-technical collaborators, and contribute to a positive team environment.

Nice To Haves

PhD in a related field preferred, especially where accompanied by demonstrated delivery of production AI systems.
Experience with time series analysis, anomaly detection, forecasting, predictive modeling, NLP/text analytics, or related applied methods preferred.
Experience developing and integrating APIs for healthcare systems to ensure seamless interaction with AI models preferred.
Experience with agentic AI patterns (tool use, orchestration, human-in-the-loop controls, policy enforcement) and maintaining those systems over time preferred.
Experience with computer vision in healthcare contexts (classification, segmentation, CNNs) and advanced generative methods (GANs) — as needed by use cases preferred.

Responsibilities

Production AI Engineering & Architecture Lead the design and engineering of production‑grade AI systems, including LLM‑based and agentic solutions, that integrate with clinical, operational, and administrative platforms.
Translate institutional goals and use cases into deployable AI architectures, defining system boundaries, APIs, infrastructure components, and operational dependencies.
Ensure AI systems are designed for reliability, security, scalability, and maintainability within St. Jude’s enterprise architecture.
Deployment, Operations, and Lifecycle Ownership Own the end‑to‑end lifecycle of AI systems from initial deployment through ongoing operation, optimization, and retirement.
Lead production deployments and ensure AI solutions are safely integrated into existing workflows without disrupting clinical or operational processes.
Establish and maintain monitoring, logging, and alerting to track system performance, usage, data drift, and failure modes.
Diagnose and resolve production issues, working closely with IT, informatics, and platform teams to maintain uptime and trust.
Responsible AI, Governance, and Reliability Implement technical controls that support responsible AI practices, including auditability, access controls, versioning, and human‑in‑the-loop safeguards.
Partner with analytics, information security, and governance teams to ensure AI systems align with institutional AI governance standards and regulatory expectations.
Document system behavior, deployment patterns, and operational procedures to support transparency, audit readiness, and long‑term sustainability.
Collaboration, Enablement, and Technical Leadership Collaborate closely with data scientists, clinicians, informaticists, and business stakeholders to translate needs into robust, operational AI solutions.
Serve as a technical mentor within Analytics Services, helping establish engineering standards, patterns, and best practices for enterprise AI.
Stay current on emerging AI technologies, platforms, and engineering approaches, evaluating their applicability to healthcare and enterprise operations.