Overview

As the Principal AI/ML Engineer for GTS, you will drive the architectural design, hands‑on development, and production delivery of scalable, resilient, and secure software solutions that integrate AI/ML capabilities into healthcare applications and workflows.

Responsibilities

• Translate business needs into measurable ML tasks. Validate data, establish simple baselines, and integrate suitable models. Demonstrate whether AI improves outcomes over simpler approaches
• Integrate generative AI and large language models (LLMs) where they add value. Use retrieval‑augmented generation (RAG), which supplies relevant source content to a model, when appropriate. Evaluate accuracy, unsupported claims, latency, and cost
• Build model‑serving interfaces and machine learning operations (MLOps) workflows for versioning, deployment, monitoring, and rollback
• Track changes in data and model performance, then improve models, prompts, or product design
• Apply responsible AI practices through documented evaluations, bias and robustness checks, privacy safeguards, and human review where needed. Address prompt injection and sensitive‑data leakage, define safe fallback behavior, and explain model limitations
• Design and build distributed services, application programming interfaces (APIs), and reliable data integrations. Make clear trade‑offs across scalability, performance, cost, and maintainability
• Write and review production code, automate unit and integration tests, and build continuous integration and delivery (CI/CD) pipelines for safe, repeatable cloud releases
• Build security and privacy into software design through access controls, secrets management, and safe data handling. Identify technical risks before release and address them with product and security partners
• Own production readiness and service health through logs, metrics, and traces. Troubleshoot incidents, remove recurring failure points, and improve reliability and response times
• Lead cross‑team architecture decisions and deliver reusable components. Document trade‑offs, mentor engineers, and break complex requirements into manageable releases that meet agreed product and quality goals
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements

Qualifications

• Bachelor's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience
• 15+ years of relevant engineering experience, including 8+ years of hands‑on software engineering and 3+ years delivering AI/ML capabilities in production
• Experience taking ML or generative AI capabilities from evaluation to production, including model integration, deployment, monitoring, and measurable quality assessment
• Solid coding skills in at least one production backend language, with working proficiency in Python for AI/ML development
• Demonstrated delivery of distributed systems, APIs, data integrations, automated tests, and cloud deployments, including practical security and operational ownership
• Ability to explain technical decisions clearly, influence architecture across teams, and mentor engineers while continuing to contribute production code

Nice to have

• Experience building healthcare applications or working with other regulated or sensitive data
• Experience delivering shared services or platforms used by multiple engineering teams
• Experience with containers, infrastructure as code, or model registries to support reliable software and model releases
• Experience with vector search or RAG evaluation, and familiarity with an established ML framework.