Senior GenAI/AI Solutions Architect

Thermo Fisher Scientific

2d • Onsite

About The Position

As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer. DESCRIPTION: We are seeking a Senior/Lead GenAI/AI Solution Architect to translate business challenges into scalable, secure, enterprise-grade AI solutions across the PSG value chain (Commercial Operations, Finance/Legal, Manufacturing, Quality, Supply Chain). This role sits within IT / Enterprise Architecture and partners with PSG business teams, Engineering, Data/Platform, Security, and Quality/Validation to take problems from concept → POC → prototype validation → enterprise deployment, operating within GxP expectations where applicable.

Requirements

• 7+ years in solution architecture and/or senior engineering roles delivering enterprise systems.
• 2+ years of experience delivering Generative AI solutions in an enterprise environment, including taking solutions from prototype to production-scale deployment.
• Demonstrated ability to take a business problem through solution concept → POC → prototype validation → enterprise scale.
• Demonstrated product and outcome orientation, with the ability to prioritize work based on measurable business impact and end-user value.
• Strong hands-on Python experience delivering GenAI systems in an enterprise environment (building services/APIs, integrations, tests, and telemetry).
• Practical experience with LangChain, LangGraph, and LangSmith (tracing, debugging, evaluation, and/or prompt/workflow regression).
• Experience implementing LLM evaluation frameworks and measurable quality gates for RAG/agentic workflows (automated testing + human review loops).
• Experience operationalizing GenAI solutions with monitoring/telemetry, prompt/version management, and evaluation-driven iteration.
• Experience working in Agile delivery environments (e.g., Scrum/Kanban), collaborating effectively with cross-functional teams through iterative development.
• Proven ability to define, quantify, and communicate value (e.g., efficiency gains, risk reduction, cost savings, cycle-time improvement) and translate outcomes into success metrics and adoption measures.

Nice To Haves

• Experience with multi-agent systems and enterprise tool execution patterns (governed tools, permissions, audit trails).
• Experience designing/operating LLMOps/MLOps foundations (versioning, monitoring, incident/rollback, model/prompt governance).
• Experience with enterprise integration patterns (APIs, IAM/security, logging/audit, reliability) and operating in regulated/quality-critical environments (GxP exposure preferred).
• Experience delivering solutions in one or more PSG domains (Commercial Ops, Finance/Legal, Manufacturing, Quality, Supply Chain).
• Certifications or deep working knowledge in AWS, Databricks, and/or Dataiku.

Responsibilities

• Business to Solution Delivery: Partner with stakeholders to define problem statements, success metrics, and solution concepts; deliver end-to-end implementations from POC to scaled enterprise deployment (security, governance, reliability, cost).
• GenAI / Agentic Architecture: Design and build advanced GenAI applications including RAG, agentic RAG, and multi-agent orchestration, integrating into existing enterprise systems and workflows.
• Hands-on Prototyping & Implementation: Build working solutions from the ground up in Python (services/APIs, integrations, testing, and telemetry) to demonstrate value quickly; iterate with users to validate usability and outcomes, then harden for production.
• Enterprise Agent Tooling & Standards: Establish reusable patterns, reference architectures, and organizational standards for agentic systems (e.g., internal enablement artifacts such as skills/standards documentation, and agent/tooling foundations).
• LLM Evaluation & Quality Gates: Implement evaluation frameworks (automated + human-in-the-loop) for retrieval quality, groundedness, accuracy, safety, latency, and cost; set up regression testing for prompts and workflows.
• Prompt & Context Engineering: Own best practices for prompt and context engineering (tool schemas, prompt/version management, context construction, retrieval tuning, and guardrails).
• Agent Interoperability Patterns: Implement agent interoperability patterns (e.g., Model Context Protocol (MCP) and agent-to-agent messaging patterns) in enterprise contexts (message contracts, routing, auditability, and boundaries).
• Platform & Integration: Work across AWS, OpenAI services, Databricks, Dataiku, and SQL ecosystems to enable data access, orchestration, deployment, and monitoring.
• GxP/Validation Partnership (as applicable): Partner with Quality/Validation and Security to support required documentation, controls, and traceability for regulated or quality-critical deployments.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees