Software Engineer II, AI Solutions & Platforms

Work Schedule

Environmental Conditions

Job Description

About the Role

At Thermo Fisher Scientific youll 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 challengesfrom 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 arescalable 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:

• Bachelors degree in computer science engineering or a related technical field. Masters 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.