Principal AI|ML Engineer

Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods and models. What’s more, we are in collaboration with leading academics, industry experts and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.

Join us to do the best work of your career and make a profound social impact as a Principal AI/ML Engineer on our Services AI Team in

What you’ll achieve As a Principal AI/ML Engineer, you will act as a senior technical authority, you will drive experimentation, architecture and model deployment for high-visibility initiatives, working closely with global teams and influencing foundational decisions for our AI stack. You will combine ML depth with practical engineering sense, solve open-ended problems and independently convert ideas into production systems.

You will:

• Lead end to end development and deployment of ML models, LLMs, and agentic frameworks into production.
• Own experimentation strategy - identify promising approaches, run experiments, and assess feasibility to turn concepts into deployable solutions.
• Build scalable data and model pipelines, covering training, evaluation, inference, and automation across cloud platforms.
• Establish LLMOps best practices, including monitoring, guardrails, model lifecycle management, and responsible AI guidelines.
• Provide technical leadership, mentoring junior contributors and partnering with cross-functional teams across EMEA, India, and the US.

Essential Requirements

• Master’s degree in data science or computer science with 10+ years of professional experience in Data Science role
• Strong Expertise in ML algorithms, generative AI, LLM architectures, and agentic frameworks (LangChain, AutoGen, LlamaIndex, etc.).
• Strong engineering skills: Python, cloud (Azure/AWS/GCP), Docker, and solid understanding of modern MLOps practices.
• Ability to work independently, make technical decisions, and navigate ambiguity without close direction.
• Strong grounding in experimental design, data workflows, and choosing the right model for the right problem.

Desirable Requirements

• PhD or published research in ML/AI, or deep expertise across ML and LLM operations (e.g., neural networks, RAG workflows, chatbot or multiagent deployments such as CrewAI/AutoGen, realtime analytics, bigdata technologies, and LLM specific ethical considerations).