Sr AIML Engineer, Applied AI

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 Senior AI/ML Engineer Applied AI you will provide hands-on technical leadership across the design development evaluation and production deployment of advanced AI/ML solutions. You will architect and build machine learning and deep learning models Large Language Model (LLM) applications Retrieval-Augmented Generation (RAG) solutions and agentic workflows that power internal and external customer-facing applications. You will work across the AI/ML lifecycle from ideation research and experimentation through data engineering model development and optimization evaluation performance tuning and deployment. Partnering closely with data scientists software engineers product teams and scientific stakeholders you will translate complex business and scientific needs into scalable reliable and impactful AI/ML capabilities.

This is a deeply hands-on individual contributor role with significant technical leadership responsibilities. Youll also mentor engineers influence platform strategy and ensure AI-driven systems are accurate through consistent evaluation frameworks engineering standards and technical best practices. A successful candidate in this role is expected to collaborate effectively with the broader teams and consistently deliver well-architected 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.

Job Description:
Key Responsibilities

• Lead activities across the AI/ML lifecycle from ideation research data engineering model development and optimization evaluation performance tuning and deployment while continuously engaging customers to gather feedback and incorporate it into solution development.
• Iteratively develop deploy and scale AI/ML models and solutions across life sciences genomics material sciences and healthcare.
• Contribute to the development of AI/ML models and solutions following established model and system architectures software design standards reusable patterns and best practices for AI and Generative AI solutions.
• Apply evaluation-driven approaches to AI/ML development by implementing and running evaluation frameworks analyzing model performance and using results to improve the quality and reliability of AI/ML solutions.
• Build and deploy LLM-powered services using Azure OpenAI Anthropic Claude and OpenAI-compatible APIs.
• Architect and implement agentic AI and RAG workflows including data ingestion chunking embeddings vector search retrieval tool calling memory and prompt engineering.
• Design develop and integrate Generative AI systems using LangChain and LangGraph for agentic workflows and orchestration.
• Integrate AI/Generative AI capabilities into enterprise platforms scientific applications and end-to-end workflows.
• Mentor and guide engineers across the AI/ML lifecycle including model development evaluation and implementation of AI solutions.
• 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.
• Stay current with advancements in AI/ML Generative AI agentic frameworks and LLM ecosystems and apply relevant innovations to enhance internal tools scientific solutions and customer-facing products.

Candidate Requirement:

Education and Experience:

• Bachelors degree in AI/ML computer science statistics engineering or a related technical field. Masters. degree preferred.
• 6 years of industry experience in software engineering and developing AI/ML solutions with a strong track record of shipping these into real production systems in a robust experimentation framework not just offline analyses or research prototypes.
• 4 years of experience working in agile/scrum environments.
• Hands-on experience in developing and applying AI techniques and algorithms including deep learning CNNs decision trees clustering ensembles and related approaches.
• Hands-on experience developing retrieval-augmented generation (RAG) and agentic AI solutions including embeddings retrieval vector search tool calling prompt engineering orchestration and evaluation.
• Strong proficiency in Python PyTorch C C# and other relevant programming languages and frameworks.
• Expertise with backend engineering best practices with demonstrated ability to design build and own reliable scalable systems that serve users.
• Experience with LangChain and LangGraph for LLM orchestration and agentic workflows.
• Strong data engineering skills including ETL/data pipelines and large-scale data processing and analysis using tools such as Pandas and NumPy.
• Ability to work closely with backend platform and application engineers on model serving pipeline architecture deployment infrastructure and production integration with sound judgement in balancing scope quality and speed to delivery.
• Demonstrated experience leveraging AI coding assistants or agents as part of your engineering workflow.
• Excellent written and verbal communication skills with the ability to explain complex technical concepts clearly.
• Flexibility and adaptability to work in a fast-paced and collaborative environment.
• Preferred: Hands-on experience developing and deploying AI/ML models and solutions for life sciences genomics materials sciences healthcare or other regulatory settings.
• Preferred: Experience with MLOps or LLMOps concepts including deployment monitoring orchestration observability and model lifecycle management.
• Nice-to-have: Experience applying AI/ML models and methods to computational biology.
• Nice-to-have: Experience with cloud platforms such as Azure AWS or GCP.