Senior Artificial Intelligence and Machine Learning Engineer

Thermo Fisher Scientific India Pvt Ltd; Bengaluru; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

Thermo Fisher Scientific India Pvt Ltd is seeking a Senior AI/ML Engineer, Applied AI, to lead design, development, evaluation, and deployment of advanced AI/ML solutions across life sciences domains.

You will architect models, build LLM-powered services, and mentor engineers while collaborating with data scientists, software engineers, and product teams to deliver production-ready AI capabilities.

Qualifications

• Bachelor’s degree in AI/ML, computer science, statistics, engineering, or a related technical field.
• Master’s degree preferred.
• 6+ years of industry experience in software engineering and developing AI/ML solutions with production impact.
• 4+ years of experience working in agile/scrum environments.
• Hands-on experience in deep learning, CNNs, decision trees, clustering, ensembles.
• Hands-on experience developing RAG and agentic AI solutions, including embeddings and vector search.
• Proficiency in Python, PyTorch, C++, C#, and related languages.
• Experience with LangChain and LangGraph for LLM orchestration.
• Strong data engineering skills with ETL/data pipelines.

Responsibilities

• Lead AI/ML lifecycle activities from ideation to deployment with customer feedback.
• Develop and scale AI/ML models and solutions across life sciences domains.
• Contribute to AI/Generative AI models and solutions following standards and patterns.
• Apply evaluation-driven approaches to improve model quality and reliability.
• Build and deploy LLM-powered services using Azure OpenAI and other APIs.
• Architect agentic AI and RAG workflows with data ingestion, embeddings, and memory.
• Design and integrate Generative AI systems using LangChain/LangGraph.
• Mentor engineers across the AI lifecycle and influence engineering standards.

Skills

Python

PyTorch

C++

LangChain

LangGraph

MLOps

NLP

Vector search

Education

Bachelor's in AI/ML

Master's preferred

Tools

Azure OpenAI

OpenAI API

Anthropic Claude

Job description

Sr AI/ML Engineer, Applied AI 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 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. 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, 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

• 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

• Bachelor’s degree in AI/ML, computer science, statistics, engineering, or a related technical field.
• Master’s. 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.
• 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.
• Experience with cloud platforms such as Azure, AWS or GCP.

Experience Level Senior Level