Work Schedule

Environmental Conditions

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, AI Solutions & Platforms, 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: Experience 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.