Engineering Manager - Generative AI

Engineering Manager - Generative AI

At Boston Scientific, we'll give you the opportunity to harness all that's within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information, and training, we'll help you in advancing your skills and career. Here, you'll be supported in progressing whatever your ambitions.

About the role: At Boston Scientific, we're advancing healthcare through innovation, and we're seeking a manager who can balance delivery and leadership to expand our AI capabilities.

In this role, you'll guide a team of AI engineers building secure LLM services and the data platforms that power them. You'll oversee delivery of production-ready solutions including RAG search, feature stores, vector databases, and model governance, all supported by mature MLOps/LLMOps practices. You'll work cross-functionally to translate clinical and business needs into compliant AI products that improve patient outcomes and drive operational efficiency. From idea to impact, you'll shape the future of AI in healthcare, securely, ethically, and at scale.

Work mode: Hybrid schedule; in-office at the local site at least three days per week. This role is not eligible for fully remote work. Visa sponsorship: Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time.

Your responsibilities will include:

• Lead an engineering organization of Data Engineers, Generative-AI Engineers, and Generative-AI Solution Architects (7+ full-time equivalents), fostering a learning-focused, high-performance culture.
• Support product teams with technical requirements and user-story definition to align engineering deliverables with clinical and regulatory needs.
• Serve as the primary liaison between business stakeholders and engineering, translating commercial and clinical priorities into actionable backlogs; communicate progress, risks, and dependencies.
• Define and execute the technical roadmap for data ingestion, feature stores, vector databases, and LLM-powered services; align outcomes to objectives and key results (OKRs) and budget.
• Oversee architecture and code reviews for RAG pipelines, fine-tuning workflows, prompt operations, and model governance to ensure scalability, security, and cost efficiency.
• Embed observability, drift monitoring, and alignment guardrails across data and model lifecycles; target 99.9% uptime and fast mean time to recovery (MTTR).
• Drive machine learning operations (MLOps) and large language model operations (LLMOps), including continuous integration/continuous delivery (CI/CD), model registries, and evaluation suites; optimize graphics processing unit (GPU) and accelerator utilization and cost.
• Partner with Product, Security, and Compliance to convert business needs into AI solutions and clearly communicate risk-reward trade-offs to executive stakeholders.
• Champion continuous learning via brown-bag sessions, conference support, and individualized career-development plans.

Required qualifications:

• Bachelor's degree in a relevant field; a science, technology, engineering, or mathematics (STEM) discipline is preferred.
• 8+ years of industry engineering experience beyond academic training.
• 4+ years managing cross-functional AI, data, or software teams with responsibility for performance and team development.
• Hands-on expertise with at least one major cloud (Amazon Web Services, Google Cloud Platform, or Microsoft Azure) and modern data stacks (Apache Spark or Apache Flink; Apache Airflow; Snowflake or BigQuery; Delta Lake).
• Deep understanding of microservices architecture, secure application programming interface (API) design, and regulated data-exchange patterns.
• Strong communication and stakeholder management skills for effective collaboration across global teams and functions.

Preferred qualifications:

• M.S. in Computer Science, Data Science, or a related field.
• Proven record delivering generative AI solutions, including LLM fine-tuning, RAG, vector search, guardrails, and evaluation frameworks.
• Certifications such as AWS Certified Data Analytics, GCP Professional Machine Learning Engineer, or Azure AI Engineer Associate.
• Experience in highly regulated domains such as healthcare, finance, or government cloud.
• Experience contributing to open-source generative AI projects or publications on enterprise AI best practices.

Compensation will be commensurate with demonstrable level of experience and training, pertinent education including licensure and certifications, among other relevant business or organizational needs.