Asks for

The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

Job 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, 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.
Life sciences or clinical research industry experience preferred.

At Thermo Fisher Scientific, we are committed to fostering a healthy and harmonious workplace for our employees. We understand the importance of creating an environment that allows individuals to excel. Please see below for the required qualifications for this position, which also includes the possibility of equivalent experience:

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.

*Must be able to pass a comprehensive background check, which includes a drug screening.

The annual salary range estimated for this position in North Carolina is $130,000-$180,000 USD. This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy.