About The 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. In this role, you will write high-quality production code, collaborate closely with product owners, business stakeholders, architects and engineering teams to translate requirements into reliable technical solutions. You will develop reusable components, services, and AI solutions that are scalable, secure, reliable, and maintainable.

Key Responsibilities

Design, develop, and deploy production-grade Generative AI, including RAG based solutions and agentic workflows.
Build and maintain Python based microservices and APIs.
Contribute hands-on to system design, including APIs, service architecture, data models, workflows, and integrations with existing backend and scientific applications.
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.
3+ years of experience in software engineering and developing AI solutions.
Strong experience building backend systems with Python and REST APIs. FastAPI experience preferred.
Experience working in agile/scrum development environments.
Proficiency with Git-based workflows, CI/CD pipelines, and automated testing strategies.
Familiarity with 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.
Experience integrating or building retrieval-augmented generation (RAG) solutions.
Strong communication and collaboration skills, with the ability to explain technical concepts clearly.
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.