Lead 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 Lead 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 drive business insights enhance scientific workflows and improve customer experiences. 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.

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 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.

This role combines deep hands-on individual contributor leadership with growing management responsibility. You will personally architect build and evaluate advanced AI/ML solutions while progressively taking on the leadership of a team as headcount grows. Hands-on contribution remains primary focus for this role making it ideal for a technical leader who wants to continue building while expanding their influence through mentorship technical direction and team leadership.

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.
• Provide technical leadership for AI/ML models platforms and solutions including defining reference model and system architectures software design standards reusable patterns and best practices for AI and Generative AI solutions.
• Champion an evaluation-driven approach to AI/ML solution development establishing rigorous evaluation practices and promoting their consistent adoption across the organization.
• 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.
• Lead manage and develop a team of AI/ML engineers and solution developers to deliver measurable outcomes in a fast-paced environment.

Candidate Requirement:

Education and Experience:

• Masters degree in AI/ML computer science statistics engineering or a related technical field. Ph.D. degree preferred.
• 10 years of industry experience in software engineering and developing AI/ML solutions with a strong track record of shipping AI/ML solutions into real production systems in a robust experimentation framework not just offline analyses or research prototypes.
• 5 years of experience working in agile/scrum environments.
• 2 years of experience leading supervising and developing technical talent in an applied AI/ML setting.
• Hands-on experience in developing and applying AI techniques and algorithms including deep learning CNNs decision trees clustering ensembles and related approaches with demonstrated experience deploying them into real production systems.
• Strong proficiency in Python PyTorch C C# and other relevant programming languages and frameworks.
• Strong data engineering skills including ETL/data pipelines and large-scale data processing and analysis using tools such as Pandas and NumPy.
• Hands-on experience developing production-grade retrieval-augmented generation (RAG) and agentic AI solutions including embeddings retrieval vector search tool calling orchestration and evaluation.
• Experience with LangChain and LangGraph for LLM orchestration and agentic workflows.
• Demonstrated experience leveraging AI coding assistants or agents as part of your engineering workflow.
• Proven 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.
• Excellent written and verbal communication skills with the ability to explain complex technical concepts clearly.
• Strong experience in people mentorship and supervision.
• 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.
• Preferred: Experience applying AI/ML models and methods to computational biology.
• Preferred: Experience with cloud platforms such as Azure AWS or GCP.