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
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Design, develop, and deploy production-grade Generative AI and backend applications using Python, FastAPI, LangChain, LangGraph and related technologies.
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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.
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Develop and maintain RAG and agentic AI workflows, including data ingestion, chunking, embeddings, vector search, retrieval, tool calling, memory, and prompt engineering.
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Integrate LLMs using Azure OpenAI, Anthropic Claude, and OpenAI-compatible APIs.
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Design and develop secure, scalable RESTful APIs and backend services with a focus on performance, reliability, authentication, rate limiting, and observability.
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Develop and optimize data pipelines using Pandas and NumPy, and implement vector-search solutions using PostgreSQL/pgvector and Qdrant.
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Collaborate with cross-functional teams, including R&D, engineering, data science, IT, Q&A, and regulatory, to define requirements, specifications, and development objectives.
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Work closely with product managers, architects, and other engineers to translate requirements into reliable solutions, and deliver against agile/scrum commitments.
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Write clean, maintainable, well-tested production code, and help troubleshoot, optimize systems as they move into production.
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Contribute to technical documentation, knowledge sharing, code reviews, and engineering best practices.
Candidate Requirement:
Education and Experience:
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Bachelor’s degree in computer science, engineering, or a related technical field. Master’s degree preferred.
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5+ years of combined experience in software engineering and developing AI solutions.
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3+ years of hands-on experience building scalable backend systems with Python and REST APIs. FastAPI experience preferred.
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3+ years of experience working in agile/scrum development environments.
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Proficiency with Git-based workflows, CI/CD pipelines, and automated testing strategies.
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Experience building ETL/data pipelines, and data processing workflows using tools such as Pandas and NumPy.
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Experience integrating LLMs using Azure OpenAI or Anthropic Claude, or OpenAI-compatible APIs.
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Hands-on experience developing retrieval-augmented generation (RAG) solutions, including embeddings, retrieval, and vector search using technologies such as PostgreSQL/pgvector or Qdrant.
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Strong communication and collaboration skills, with the ability to explain technical concepts clearly.
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Nice-to-Have: Familiarity with LangChain and LangGraph for developing agentic applications.
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Nice-to-Have: Familiarity with MLOps tools (MLflow, Kubeflow), ML Frameworks (scikit-learn, PyTorch), and model evaluation frameworks.
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Nice-to-Have: Experience developing and deploying applications on cloud platforms such as Azure, AWS, or GCP.
