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, Applied AI Solutions, 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.