Job Description
About the position 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. • 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. Requirements • 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. Nice-to-haves • 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. Benefits • 401k • health_insurance • dental_insurance • vision_insurance • life_insurance • disability_insurance • paid_holidays • paid_volunteer_time • tuition_reimbursement • professional_development Apply tot his job