Sr. Staff AI Architect

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description About Company: ThermoFisher Scientific Inc. is the world leader in serving science, with revenues of more than $25 billion and approximately 1,30,000 employees globally. We help our customers accelerate life sciences research, solve elite analytical challenges, improve patient diagnostics, deliver medicines to market, and increase laboratory efficiency. Through our world-class brands—Thermo Scientific, Applied Biosystems, Invitrogen, Fisher Scientific, and Unity Lab Services—we offer an unmatched combination of innovative technologies, purchasing convenience, and comprehensive services. About Team: We are the Digital Foundation Platform team - the software center of excellence (CoE) for Thermo Fisher Scientific. We are responsible for developing and delivering foundational components and SaaS-based applications and digital lab (Cloud-based) tools, foundational AI and AI agentic solutions, to help scientists do their work more efficiently and with precision, enabling them to make our world healthier, cleaner, and safer. Our elite software products and solutions accelerate scientific discovery and lab productivity. These solutions - Provide rich content, selection tools, teamwork tools, and scientific apps that allow our customers to focus on innovation and the complexities of their science. - Build a connected world for our customers where discoveries happen in a thoughtful way — where every device/product is connected, self-aware, and self-healing—thereby enabling efficient workflows and collaborative science. - Enable our customers to efficiently handle their labs by providing them with insight into workflow processes, asset uptime, and product availability. We give them the flexibility to access what they need when they need it, allowing them to select and receive products and services across multiple channels. We apply industry-standard methodologies to the design, development, and deployment of best-in-class software products built to demonstrate the power and scalability of the cloud. Purpose: The Senior Staff Architect provides enterprise-wide architectural, design, and technical leadership for Generative AI and agentic AI solutions across multiple Scrum teams. As a strategic technical leader and hands-on architect, you will define the architecture, standards, and roadmap for an enterprise agentic harness that enables agents to reason, plan, use tools, access knowledge, maintain state, collaborate, and operate safely at scale. The role requires deep expertise in designing production-grade agentic systems , cloud-native platforms, RAG architectures, LLM integrations, and AI engineering practices. You will influence platform strategy, mentor architects and engineers, and ensure AI-driven systems are scalable, secure, resilient, observable, governable, and production-ready . Key Responsibilities Technical Leadership, Architecture & Design Provide enterprise architecture leadership for Generative AI and agentic AI platforms across multiple teams. Define the architecture and roadmap for a reusable agentic harness and runtime platform . Own high-level and low-level system design , including component architecture, data flows, integration patterns, deployment topologies, and runtime interactions. Design and evolve cloud-native, event-driven, API-first, and distributed architectures for AI-enabled products. Define reference architectures, design standards, and best practices for: Agentic systems Multi-agent orchestration RAG and knowledge-grounded agents Tool and function execution Agent memory and state management Human-in-the-loop workflows Agent-to-Agent communication Define architecture patterns for agent planning, task decomposition, reasoning, execution, retries, timeouts, compensation, and failure recovery. Establish standards for agent lifecycle management, including agent creation, versioning, testing, evaluation, deployment, monitoring, and retirement. Act as the go-to authority for architecture, design trade-offs, scalability decisions, and complex implementation challenges. Ensure architectural decisions address Non-Functional Requirements (NFRs) , including performance, scalability, security, reliability, privacy, governance, cost, and observability. Lead architecture reviews, design reviews, technical investigations, and architecture decision records. Agentic AI and Generative AI Engineering Design and implement agentic systems using LangGraph , LangChain , and comparable orchestration frameworks. Architect the core capabilities of an agentic harness, including: Agent runtime and execution management Planning and task orchestration Tool registry and tool discovery Function calling and API integration Agent memory and context management Workflow state persistence Multi-agent collaboration Human approvals and intervention Guardrails and policy enforcement Execution tracing, auditability, and replay Design model-agnostic architectures supporting Azure OpenAI , Anthropic Claude , OpenAI-compatible APIs, and local models using Ollama . Define model routing, fallback, provider abstraction, latency optimization, token management, and cost-control strategies. Architect and implement Retrieval-Augmented Generation (RAG) solutions, including document ingestion, chunking strategies, embeddings, retrieval, reranking, grounding, and response synthesis. Integrate agents with APIs, enterprise systems, scientific applications, structured data, knowledge graphs, and domain ontologies. Apply advanced prompt engineering, structured outputs, tool-calling, memory patterns, and context optimization techniques. Define safe and reliable patterns for autonomous and semi-autonomous agent execution. Agent Evaluation, LLMOps & Production Excellence Define automated testing and evaluation strategies for agentic and GenAI systems. Establish evaluation frameworks for: Task completion Tool-use accuracy Response correctness Grounding and retrieval quality Safety and policy compliance Robustness and recovery Latency and cost Design regression testing for prompts, models, tools, workflows, and agent behaviors. Define observability standards using execution traces, agent steps, tool calls, model interactions, latency, token usage, cost, and failure metrics. Establish mechanisms for agent debugging, replay, inspection, and root-cause analysis. Define secure deployment, monitoring, governance, and operational practices for AI systems. Ensure p