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 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.