Staff Software Engineer, 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 Staff Engineer AI Solutions & Platforms you will provide end-to-end architectural design and technical leadership across multiple teams delivering enterprise-grade AI and Generative AI solutions. As a hands-on technical leader and architect you will own system design decisions define reference architectures and guide implementation of AI-powered capabilities including deep learning models LLMs RAG solutions and agentic workflows across internal and external customer-facing applications. Youll also mentor engineers influence platform strategy and ensure AI-driven systems are secure resilient and production-ready. A successful candidate in this role is expected to collaborate effectively with the broader teams and consistently deliver well-architected scalable secure production-grade AI and Generative AI features supporting a variety of use cases and scientific products with measurable impact on scientific workflows customer outcomes and innovation velocity.

  Key Responsibilities

• Provide software and systems architecture leadership including reference architectures design standards patterns and best practices for AI and Generative AI platforms and solutions.
• Own high-level and low-level system design including component architecture data flows integration patterns and deployment strategies.
• Design and evolve cloud-native event-driven and API-first architectures for AI-enabled products and platforms.
• Design develop and integrate Generative AI systems using LangChain and LangGraph for agentic workflows and orchestration.
• Architect and implement agentic AI and RAG workflows including data ingestion chunking embeddings vector search retrieval tool calling memory and prompt engineering.
• Build and deploy LLM-powered services using Azure OpenAI Anthropic Claude and OpenAI-compatible APIs.
• Leverage Ollama for local and on-prem LLM system experimentation and evaluation.
• Integrate AI/Generative AI capabilities across enterprise platforms and scientific applications and workflows.
• Actively contribute to hands-on development using Python and modern backend frameworks such as FastAPI.
• Design and build well-structured maintainable and extensible APIs supporting AI and data-driven workloads.
• Define and implement performance scalability security reliability and observability patterns for AI-driven services.
• Define and implement automated testing and evaluation strategies for Generative AI systems including prompt testing regression testing and model evaluation pipelines.
• Partner closely with product managers architects and other engineers to translate requirements into reliable solutions and deliver against agile/scrum commitments.
• Mentor and guide engineers on software architecture system design and advanced Generative AI patterns.
• Actively participate in Communities of Practice influencing engineering standards and AI/Generative AI adoption strategies across the organization.
• Communicate effectively with technical and non-technical stakeholders through clear documentation architecture diagrams and design reviews.
  

• Candidate Requirement:

  Education and Experience:

• Bachelors degree in computer science engineering or a related technical field. Masters degree preferred.
• 10 years of industry experience in software engineering and developing AI solutions including multiple years specializing in integrating production-grade AI/ML solutions.
• 5 years of experience working in agile/scrum environments.
• 5 years of hands-on experience building scalable backend systems with Python and REST APIs (FastAPI preferred).
• Proficiency with Git-based development workflows CI/CD pipelines and automated testing strategies.
• Proficiency with containerization and orchestration (Docker Kubernetes).
• Practical experience integrating and operating LLMs using Azure OpenAI or Anthropic Claude or OpenAI-compatible APIs.
• Experience using Ollama or similar technologies for local and on-premises inference experimentation and evaluation.
• Hands-on experience developing retrieval-augmented generation (RAG) and agentic AI solutions including embeddings retrieval vector search tool calling prompt engineering and evaluation.
• Experience with LangChain and LangGraph for LLM orchestration and agentic workflows.
• Experience designing and managing data stores and vector indexes supporting GenAI and RAG workloads using technologies such as PostgreSQL/pgvector and Qdrant.
• Strong data engineering skills including ETL/data pipelines and large-scale data processing and analysis using tools such as Pandas and NumPy.
• Excellent written and verbal communication skills with the ability to explain complex technical concepts clearly.
• Preferred: Experience with MLOps tools (MLflow Kubeflow) ML Frameworks (scikit-learn PyTorch) model evaluation frameworks and model governance.
• Preferred: Experience with cloud platforms such as Azure AWS or GCP.