About this role
Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description About the Role At Thermo Fisher Scientific, you’ll do meaningful work that makes a positive global impact. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner, and safer. With industry-leading R&D investment, we empower our teams to solve complex scientific challenges—from environmental protection to advancing healthcare and cancer research.
As a Staff Engineer, Applied AI Solutions, you will provide end-to-end architectural, design, and technical leadership across multiple teams delivering enterprise-grade AI and Generative AI solutions. As a hands-on technical leader and architect, you will own system design decisions, define reference architectures, and guide implementation of AI-powered capabilities, including deep learning models, LLMs, RAG solutions and agentic workflows across internal and external customer-facing applications. You’ll also mentor engineers, influence platform strategy, and ensure AI-driven systems are secure, resilient and production-ready.
A successful candidate in this role is expected to collaborate effectively with the broader teams, and consistently deliver well-architected, scalable, secure, production-grade AI and Generative AI features supporting a variety of use cases and scientific products, with measurable impact on scientific workflows, customer outcomes and innovation velocity. Key Responsibilities Provide software and systems architecture leadership including reference architectures, design standards, patterns, and best practices for AI and Generative AI platforms and solutions. Own high-level and low-level system design, including component architecture, data flows, integration patterns, and deployment strategies.
Design and evolve cloud-native, event-driven, and API-first architectures for AI-enabled products and platforms. Design, develop, and integrate Generative AI systems using LangChain and LangGraph for agentic workflows and orchestration. Architect and implement agentic AI and RAG workflows, including data ingestion, chunking, embeddings, vector search, retrieval, tool calling, memory, and prompt engineering.
Build and deploy LLM-powered services using Azure OpenAI, Anthropic Claude, and OpenAI-compatible APIs. Leverage Ollama for local and on-prem LLM system experimentation and evaluation. Integrate AI/Generative AI capabilities across enterprise platforms, and scientific applications and workflows.
Actively contribute to hands-on development using Python and modern backend frameworks such as FastAPI. Design and build well-structured, maintainable, and extensible APIs supporting AI and data-driven workloads. Define and implement performance, scalability, security, reliability, and observability patterns for AI-driven services.
Define and implement automated testing and evaluation strategies for Generative AI systems, including prompt testing, regression testing, and model evaluation pipelines. Partner closely with product managers, architects, and other engineers to translate requirements into reliable solutions, and deliver against agile/scrum commitments. Mentor and guide engineers on software architecture, system design and advanced Generative AI patterns.
Actively participate in Communities of Practice, influencing engineering standards and AI/Generative AI adoption strategies across the organization. Communicate effectively with technical and non-technical stakeholders through clear documentation, architecture diagrams and design reviews. Candidate Requirement: Education and Experience: Bachelor’s degree in computer science, engineering, or a related technical field.
Master’s degree preferred. 10+ years of industry experience in software engineering and developing AI solutions, including multiple years specializing in integrating production-grade AI/ML solutions. 5+ years of experience working in agile/scrum environments.
5+ years of hands-on experience building scalable backend systems with Python and REST APIs (FastAPI preferred). Proficiency with Git-based development workflows, CI/CD pipelines, and automated testing strategies. Proficiency with containerization and orchestration (Docker, Kubernetes).
Practical experience integrating and operating LLMs using Azure OpenAI or Anthropic Claude, or OpenAI-compatible APIs. Experience using Ollama or similar technologies for local and on-premises inference, experimentation, and evaluation. Hands-on experience developing retrieval-augmented generation (RAG) and agentic AI solutions, including embeddings, retrieval, vector search, tool calling, prompt engineering, and evaluation.
Experience with LangChain and LangGraph for LLM orchestration and agentic workflows. Experience designing and managing data stores and vector indexes supporting GenAI and RAG workloads using technologies such as PostgreSQL/pgvector and Qdrant. Strong data engineering skills, including ETL/data pipelines and large-scale data processing and analysis using tools such as Pandas and NumPy.
Excellent written and verbal communication skills, with the ability to explain complex technical concepts clearly. Preferred: Familiarity with MLOps tools (MLflow, Kubeflow), ML Frameworks (scikit-learn, PyTorch), model evaluation frameworks, and model governance. Preferred: Experience with cloud platforms such as Azure, AWS or GCP.