Senior Manager, Applied AI

About this role

Job Title: Senior Manager, Applied AI
Job Location: Remote, North Carolina, USA
Job Location Type: Remote
Job Contract Type: Full-time
Job Seniority Level:
Work Schedule
Standard (Mon-Fri)Environmental Conditions
OfficeJob Description
Today, biotech and biopharmaceutical companies face significant challenges in drug development. At Clinical Research Group (CRG), part of Thermo Fisher Scientific, our Drug Development Digital Solutions are transforming clinical research through purpose-built, CRO-owned technology integrated across the clinical development journey. Our AI-enabled solutions help enable faster study startups, smarter site selection, cleaner data, greater transparency and streamlined regulatory compliance.

We are seeking a Senior Manager, Applied AI to provide technical and delivery leadership for a growing portfolio of artificial intelligence capabilities supporting internal business teams across our organization.

Working at the intersection of AI strategy, product delivery and technical execution, this leader will translate business needs into practical, secure and scalable AI capabilities spanning generative AI, agentic AI, machine learning and intelligent workflows. The role requires strong technical depth and hands-on engagement with AI architecture, engineering, data and platform decisions, while leading and developing a multidisciplinary team and providing end-to-end accountability across multiple AI initiatives.

A key focus will be evolving AI delivery from individual solutions toward a scalable, reusable technical operating model—driving reuse of AI components, services, platforms and architectural patterns; strengthening evaluation, observability and governance practices; improving engineering, development and deployment standards; and ensuring solutions deliver measurable business value, reliability and responsible use.

What you'll do:

•
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.

What you need:

• 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,…