Note: The job is a remote job and is open to candidates in USA. Thermo Fisher Scientific is seeking a Senior Manager, Applied AI to provide technical and delivery leadership for a portfolio of enterprise AI initiatives supporting internal business teams. The role will lead multidisciplinary teams, develop reusable AI platforms and standards, and oversee the delivery, evaluation, governance, and continuous improvement of secure, scalable production AI solutions.
Responsibilities
- Lead a portfolio of applied AI initiatives from opportunity definition and prioritization through technical design, development, production deployment, adoption and continuous improvement, establishing clear technical roadmaps, delivery plans, measures of success and resource priorities across the portfolio
- Provide technical and delivery leadership across generative AI, agentic AI, machine learning and intelligent automation, translating business opportunities and workflow challenges into scalable capabilities and making or guiding key decisions across solution architecture, model and platform selection, data and knowledge architecture, orchestration, evaluation and production engineering
- Lead, coach and develop multidisciplinary technical teams, including AI engineers and data scientists, providing technical direction and mentorship while establishing clear ownership and accountability and allocating resources based on business value, technical complexity, risk, dependencies and capacity
- Drive reusable enterprise AI capabilities, including platforms, components, services, APIs and architectural patterns, to accelerate delivery, establish consistent engineering practices, reduce future solution costs and prevent unnecessary duplication across teams and technology investments
- Lead the design and implementation of enterprise-grade AI solutions leveraging large language models, retrieval-augmented generation (RAG), AI agents, orchestration frameworks, machine learning and emerging technologies, with deep engagement across solution architecture, data integration, knowledge and semantic layers, model and platform selection, AI/ML engineering, security considerations and production deployment
- Establish and continuously improve robust AI evaluation and operational practices, including technical testing, model and application evaluation, monitoring, observability, performance measurement, reliability engineering and continuous improvement to ensure solutions remain accurate, reliable, secure and fit for purpose in production
- Embed governance and responsible AI practices throughout the AI solution lifecycle, incorporating appropriate data protection, security, privacy, validation and enterprise compliance requirements into architecture, engineering, testing, deployment and ongoing operations
- Establish and evolve technical standards, reference architectures and development patterns for enterprise AI, helping teams make consistent decisions across model selection, data and knowledge architecture, prompt and agent design, orchestration, evaluation, deployment, monitoring and lifecycle management
- Partner across Product, Digital, Data, Architecture, Security, Quality, Compliance and business teams, while managing delivery accountability across strategic technology partners, vendors and contingent resources and communicating effectively with executive stakeholders
- Provide technical oversight across the AI development lifecycle, including solution design, engineering practices, model and application evaluation, testing, deployment, observability, incident response and lifecycle management, ensuring production AI systems meet defined standards for performance, reliability, security and maintainability
Skills
- Bachelor's degree with 8-10 years of experience in artificial intelligence, machine learning, data science, software engineering, computer science or a related technology field. Or Advanced degree plus 7 years of relevant experience, as stated above
- Demonstrated experience leading AI or machine learning teams and complex technology portfolios in an enterprise environment, including multiple concurrent initiatives, technical prioritization, architecture and design decisions, resource allocation and delivery accountability
- Proven experience taking AI solutions from concept and experimentation through production deployment, adoption and ongoing operation at enterprise scale
- Strong understanding of generative AI and large language model technologies, including RAG, agentic workflows, tool use, orchestration, grounding, model selection, prompt and context engineering, and production implementation patterns
- Strong understanding of machine learning, data science and modern AI architecture, with sufficient depth to lead and challenge technical design decisions, evaluate solution quality and provide credible technical direction to engineering and data science teams
- Experience with major cloud and AI platforms such as Azure, AWS or GCP, as well as modern data, application and AI architectures, with the ability to evaluate technology and platform choices in the context of enterprise-scale AI solutions
- Working knowledge of Python and modern AI/ML development frameworks, with the ability to engage deeply in technical designs, review implementation approaches, evaluate code and engineering practices, and provide technical direction without serving as the primary hands-on developer
- Experience designing or operating at scale reusable AI platforms, services, APIs, data pipelines or shared technical capabilities, with demonstrated understanding of AI evaluation, monitoring, model and application performance, data quality, observability and production reliability
- Experience operating within data governance, security, privacy, regulatory and/or responsible AI frameworks
- Experience managing external technology partners, consultants or distributed delivery teams, with strong product and business acumen and the ability to connect technology investments to measurable business outcomes
- Excellent communication and stakeholder-management skills, including the ability to explain complex AI concepts, tradeoffs and risks to technical and executive audiences, lead through ambiguity and adapt as technologies and priorities evolve
- Able to communicate, receive, and understand information and ideas with diverse groups of people in a comprehensible and reasonable manner
- Able to work upright and stationary for typical working hours
- Ability to use and learn standard office equipment and technology with proficiency
- Able to perform successfully under pressure while prioritizing and handling multiple projects or activities
- May require as-needed travel (0-20%)
- Location: Remote USA. Relocation assistance is NOT provided
- *Must be legally authorized to work in the United States without sponsorship
- Life sciences or clinical research industry experience preferred
Benefits
- Variable annual bonus based on company, team, and/or individual performance results in accordance with company policy
- Remote USA work arrangement
Company Overview