Lead AI/ML Engineer, Applied AI

Thermo Fisher Scientific Inc.; India; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Thermo Fisher Scientific Inc. is seeking a Lead AI/ML Engineer, Applied AI, in India.

You will provide hands-on technical leadership across the design, development, evaluation, and production deployment of advanced AI/ML solutions, architecting models and LLM applications that power scientific workflows and customer experiences. You will mentor engineers, influence platform strategy, and ensure rigorous evaluation, with a focus on scalable, secure, and production-grade AI features.

Qualifications

• Master’s degree in AI/ML, CS, statistics, engineering or related field; PhD preferred.
• 10+ years of software engineering and AI/ML experience with production systems.
• 5+ years working in agile/scrum environments.
• 2+ years leading, supervising and developing technical talent in AI/ML.
• Hands‑on experience deploying AI techniques (DL, CNNs, ensembles) into production.
• Proficiency in Python, PyTorch, C++, C# and related languages.
• Experience with MLOps/LLMOps and LangChain/LangGraph is a plus.

Responsibilities

• Lead AI/ML lifecycle activities from ideation to deployment with stakeholder feedback.
• Develop, deploy and scale AI/ML models and LLM applications across scientific domains.
• Provide technical leadership for AI platforms, reference models, and best practices.
• Establish evaluation-driven approaches and standardize evaluation practices.
• Mentor engineers and guide AI/Generative AI adoption across the organization.
• Build LLM-powered services using Azure OpenAI and OpenAI-compatible APIs.
• Design agentic AI/RAG workflows with data ingestion, embeddings, and vector search.
• Collaborate with backend/platform engineers on deployment and production integration.
• Communicate architecture through docs and diagrams; participate in Communities of Practice.

Skills

AI/ML leadership

Python

PyTorch

Team mentorship

Communication

Agile/Scrum

Data engineering

C++

Education

Master’s degree in AI/ML, CS, statistics or engineering

Ph.D. degree preferred

Tools

Azure OpenAI

LangChain

LangGraph

OpenAI APIs

Vector search

Git

Job description

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

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 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. You’ll 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.

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.

Education and Experience

• Master’s 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.