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 Software Engineer, AI Solutions & Platforms, you will play a hands-on technical role in designing, developing, and delivering enterprise-grade AI and Generative AI solutions. You will build production-ready AI services, integrating AI models, including Large Language Models, Retrieval-Augmented Generation (RAG) solutions and agentic workflows into internal and external customer-facing systems. You will also contribute to system design and development using modern AI frameworks, backend technologies, and cloud platforms.
As a Senior Engineer, you will write production code, collaborate closely with architects and engineering teams, and contribute to technical and design decisions. You will help build AI-driven systems that are scalable, secure, reliable, and maintainable. A successful candidate in this role is expected to develop production-grade AI and Generative AI features, build reliable and high-performing backend services supporting AI workloads, contribute to high-quality code, collaborate effectively with the broader teams, and make a meaningful impact on our products and customer experiences.
Key Responsibilities
• Design, develop, and deploy production-grade Generative AI and backend applications using Python, FastAPI, LangChain, LangGraph and related technologies. • Contribute hands-on to low- and mid-level system design, including APIs, service architecture, data models, workflows, and integrations with existing backend and scientific applications. • Develop and maintain RAG and agentic AI workflows, including data ingestion, chunking, embeddings, vector search, retrieval, tool calling, memory, and prompt engineering. • Integrate LLMs using Azure OpenAI, Anthropic Claude, and OpenAI-compatible APIs. • Design and develop secure, scalable RESTful APIs and backend services with a focus on performance, reliability, authentication, rate limiting, and observability. • Develop and optimize data pipelines using Pandas and NumPy, and implement vector-search solutions using PostgreSQL/pgvector and Qdrant. • Collaborate with cross-functional teams, including R&D, engineering, data science, IT, Q&A, and regulatory, to define requirements, specifications, and development objectives. • Work closely with product managers, architects, and other engineers to translate requirements into reliable solutions, and deliver against agile/scrum commitments. • Write clean, maintainable, well-tested production code, and help troubleshoot, optimize systems as they move into production. • Contribute to technical documentation, knowledge sharing, code reviews, and engineering best practices.
Candidate Requirement: Education and Experience:
• Bachelor’s degree in computer science, engineering, or a related technical field. Master’s degree preferred. • 5+ years of combined experience in software engineering and developing AI solutions. • 3+ years of hands-on experience building scalable backend systems with Python and REST APIs. FastAPI experience preferred. • 3+ years of experience working in agile/scrum development environments. • Proficiency with Git-based workflows, CI/CD pipelines, and automated testing strategies. • Experience building ETL/data pipelines, and data processing workflows using tools such as Pandas and NumPy. • Experience integrating LLMs using Azure OpenAI or Anthropic Claude, or OpenAI-compatible APIs. • Hands-on experience developing retrieval-augmented generation (RAG) solutions, including embeddings, retrieval, and vector search using technologies such as PostgreSQL/pgvector or Qdrant. • Strong communication and collaboration skills, with the ability to explain technical concepts clearly. • Nice-to-Have: Familiarity with LangChain and LangGraph for developing agentic applications. • Nice-to-Have: Familiarity with MLOps tools (MLflow, Kubeflow), ML Frameworks (scikit-learn, PyTorch), and model evaluation frameworks. • Nice-to-Have: Experience developing and deploying applications on cloud platforms such as Azure, AWS, or GCP.