Forward Deployed Engineer

Thermo Fisher Scientific•Raleigh, NC

55d•Onsite

About The Position

Thermo Fisher Scientific is seeking a Forward Deployed Engineer / AI Solution Architect to accelerate the design, deployment, and scaling of AI-enabled solutions across the enterprise. This role will operate at the intersection of business strategy, enterprise architecture, AI engineering, and product execution, helping translate high-value business needs into secure, scalable, reusable technical solutions. The ideal candidate brings the hands-on technical depth of a forward deployed engineer, the systems thinking of a solution architect, and the stakeholder fluency required to operate across a large, complex global enterprise. This role will partner closely with business teams, IT, data owners, security, enterprise architecture, platform owners, and AI governance groups to identify use cases, shape solution approaches, build prototypes, guide production delivery, and create repeatable patterns that can scale across Thermo Fisher. This is not a traditional advisory-only architecture role. The person in this role must be comfortable moving from ambiguity to action: understanding workflows, scoping opportunities, building or guiding prototypes, making practical architecture tradeoffs, and ensuring solutions are designed for adoption, maintainability, compliance, and enterprise scale. Purpose of the Role: Thermo Fisher has significant opportunity to apply AI, generative AI, automation, agents, and data-driven solutions across business and functional processes. However, many high-value use cases require a bridge between business problem definition and technical execution. This role fills that gap by helping teams move from ideas and pilots to production-ready solutions that fit Thermo Fisher’s architecture, data landscape, security requirements, operating model, and long-term technology strategy. The Forward Deployed Engineer / AI Solution Architect will help ensure that AI solutions are not built as isolated one-off experiments, but as scalable, governed, reusable capabilities that create measurable business value.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field, or equivalent practical experience.
5+ years of experience in software engineering, solution architecture, technical deployment, enterprise architecture, AI engineering, data engineering, or a similar technical role.
Experience designing and delivering complex technical solutions in large enterprise environments.
Hands-on experience with AI, generative AI, machine learning, automation, API-based integrations, or modern application development.
Ability to write, review, or guide production-quality code using languages such as Python, JavaScript/TypeScript, Java, or similar.
Experience with cloud architecture, APIs, identity and access management, data integration, application integration, and enterprise security considerations.
Strong understanding of how large language models, AI agents, retrieval-augmented generation, prompt engineering, evaluations, and model behavior affect solution design and user experience.
Demonstrated ability to work with business stakeholders to understand workflows, define requirements, and translate needs into technical solutions.
Strong communication skills with the ability to explain complex concepts to technical and non-technical audiences.
Ability to operate in ambiguity, make sound tradeoffs, and drive work from concept through delivery.
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.

Nice To Haves

Experience deploying AI or generative AI solutions in a regulated, global, or highly matrixed enterprise environment.
Experience with ChatGPT Enterprise, OpenAI APIs, Azure OpenAI, Microsoft Copilot, agent frameworks, or similar AI platforms.
Experience with enterprise data platforms, knowledge management systems, document repositories, CRM, ERP, commercial platforms, laboratory systems, supply chain platforms, or other large-scale business systems.
Understanding of data governance, privacy, cybersecurity, regulatory, compliance, and responsible AI considerations.
Experience creating reusable architecture patterns, technical playbooks, or internal developer enablement materials.
Experience working in life sciences, healthcare, diagnostics, manufacturing, commercial operations, or scientific/technical domains.
Familiarity with Thermo Fisher’s internal technology landscape, operating model, business groups, governance processes, and architecture standards.

Responsibilities

Use Case Discovery and Technical Scoping: Partner with AI transformation team, business and functional stakeholders to deeply understand workflows, pain points, decision processes, data needs, and measurable outcomes. Translate business needs into clear technical opportunities, solution hypotheses, architecture options, and delivery plans. Assess use cases for feasibility, business value, data readiness, integration complexity, risk, scalability, and alignment with Thermo Fisher’s AI strategy. Partner with AI transformation and business stakeholders to define success metrics for AI solutions, including adoption, productivity impact, workflow improvement, quality, cycle time, cost reduction, risk reduction, or improved user experience. Help prioritize AI opportunities based on value, complexity, reusability, and enterprise applicability.
Solution Architecture and Design: Design scalable AI solution architectures that align with Thermo Fisher’s enterprise architecture, security standards, data governance, integration patterns, and platform strategy. Create end-to-end technical designs covering user experience, AI model or platform selection, data access, APIs, orchestration, integrations, security controls, human-in-the-loop processes, monitoring, and support model. Ensure solutions are designed for reuse across teams, functions, and business groups where possible. Partner with enterprise architecture, cybersecurity, data architecture, infrastructure, cloud, and application teams to ensure solutions fit within Thermo Fisher’s technical ecosystem. Identify when to use existing enterprise platforms, when to extend current capabilities, and when a new pattern or capability is required.
Prototyping, Build, and Production Deployment: Lead or directly contribute to rapid prototypes, proof-of-concepts, and minimum viable solutions that demonstrate business value. Work hands-on with AI tools, APIs, automation platforms, enterprise systems, and data sources to validate solution approaches. Partner with engineering and delivery teams to move successful prototypes into production-ready solutions. Guide teams through technical tradeoffs across speed, scalability, cost, reliability, user experience, and governance. Ensure production solutions include appropriate documentation, monitoring, ownership, support model, risk controls, and measurement approach.
AI, Agent, and Generative AI Enablement: Support the design and deployment of AI agents, GPTs, copilots, workflow assistants, automation patterns, and AI-enabled applications. Help define reusable patterns for common AI solution types, such as knowledge assistants, document processing, workflow automation, decision support, data analysis, software development support, and agentic process execution. Advise teams on effective prompt design, retrieval-augmented generation, model selection, evaluation, grounding, workflow orchestration, and responsible use. Partner with AI governance and platform teams to ensure solutions are compliant, secure, and aligned with enterprise AI standards. Create practical guidance, templates, and playbooks that help teams build AI solutions consistently and responsibly.
Reusable Patterns and Scaling: Codify successful solution approaches into reusable reference architectures, design patterns, components, templates, playbooks, and implementation guidance. Identify opportunities to consolidate similar use cases into shared platforms or enterprise capabilities. Reduce duplication by connecting teams working on similar AI and automation problems. Capture lessons learned from pilots and deployments to improve future solution design. Create feedback loops from real-world deployments into enterprise AI platform strategy, governance standards, data strategy, and product roadmaps.