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.