Boston Scientific is looking to expand its AI capabilities by hiring a manager who can balance delivery and leadership to guide a team of AI engineers in building secure LLM services and the data platforms that power them, ultimately translating clinical and business needs into compliant AI products that improve patient outcomes and drive operational efficiency.
Requirements
- 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.
- Proven record delivering generative AI solutions, including LLM fine-tuning, RAG, vector search, guardrails, and evaluation frameworks.
- Experience contributing to open-source generative AI projects or publications on enterprise AI best practices.
- Certifications such as AWS Certified Data Analytics, GCP Professional Machine Learning Engineer, or Azure AI Engineer Associate.
- 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.
Responsibilities
- 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.
- 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.
- 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.
Other
- Hybrid schedule; in-office at the local site at least three days per week. This role is not eligible for fully remote work.
- Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time.
- Bachelor’s degree in a relevant field; a science, technology, engineering, or mathematics (STEM) discipline is preferred.
- Strong communication and stakeholder management skills for effective collaboration across global teams and functions.
- Experience in highly regulated domains such as healthcare, finance, or government cloud.
