Forward Deployed Engineer

Thermo Fisher Scientific • Austin, TX

9d • Remote

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 works at the intersection of business strategy, enterprise architecture, AI engineering, and product execution, translating business needs into secure, scalable, reusable technical solutions. The role partners 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 develop repeatable patterns.

The position bridges business problem definition and technical execution, helping move AI, generative AI, automation, agent, and data-driven ideas from pilots to production-ready solutions aligned with Thermo Fisher’s architecture, data landscape, security requirements, operating model, and technology strategy. It is hands-on rather than advisory-only: the successful candidate will understand workflows, scope opportunities, build or guide prototypes, make architecture tradeoffs, and support adoption, maintainability, compliance, and enterprise scale.

The role includes discovering and prioritizing use cases; designing end-to-end AI solution architectures; prototyping and supporting production deployment; enabling AI agents, GPTs, copilots, workflow assistants, and AI-enabled applications; and codifying successful approaches into reusable patterns, templates, and playbooks. Key deliverables include reference architectures, technical scoping documents, solution blueprints, prototypes, production handoff documentation, reusable implementation guidance, platform and architecture recommendations, and approaches for measuring adoption, value, and performance.

The position is remote and based in Austin, Texas. Relocation assistance is not provided. Candidates must be legally authorized to work in the United States without sponsorship and must pass a comprehensive background check, including drug screening.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field, or equivalent practical experience.
• At least 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 Python, JavaScript/TypeScript, Java, or similar languages.
• 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 and ability to explain complex concepts to technical and non-technical audiences.
• Ability to operate amid 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, including 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 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

• Partner with AI transformation, business, and functional stakeholders to understand workflows, pain points, decision processes, data needs, and measurable outcomes.
• Translate business needs into 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.
• Define success metrics for AI solutions, including adoption, productivity, workflow improvement, quality, cycle time, cost reduction, risk reduction, and user experience.
• Prioritize AI opportunities based on value, complexity, reusability, and enterprise applicability.
• Design scalable AI solution architectures aligned with 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.
• Design solutions for reuse across teams, functions, and business groups where possible.
• Partner with enterprise architecture, cybersecurity, data architecture, infrastructure, cloud, and application teams to fit solutions into Thermo Fisher’s technical ecosystem.
• Determine whether to use existing enterprise platforms, extend current capabilities, or establish a new pattern or capability.
• Lead or contribute directly 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 technical tradeoffs involving speed, scalability, cost, reliability, user experience, and governance.
• Ensure production solutions have appropriate documentation, monitoring, ownership, support models, risk controls, and measurement approaches.
• Support the design and deployment of AI agents, GPTs, copilots, workflow assistants, automation patterns, and AI-enabled applications.
• Define reusable patterns for knowledge assistants, document processing, workflow automation, decision support, data analysis, software development support, and agentic process execution.
• Advise teams on 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 for consistent and responsible AI solution development.
• Codify successful 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 and connect teams working on similar problems.
• Capture lessons learned from pilots and deployments to improve future solution design.
• Create feedback loops from deployments into enterprise AI platform strategy, governance standards, data strategy, and product roadmaps.
• Develop reference architectures, technical scoping documents, solution blueprints, working prototypes, production handoff documentation, reusable playbooks, platform recommendations, and measurement approaches.

Benefits

• Competitive remuneration
• Annual incentive plan bonus scheme
• Healthcare
• Company 401k
• A range of employee benefits
• Career and development prospects
• Reasonable accommodation during the application or interview process, for critical job functions, and to receive other benefits and privileges

What This Job Offers

• Job Type: Full-time
• Career Level: Senior