Senior Data Science Engineer -AI at Dunnhumby Limited

Gurugram, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

14 Jun, 26

Salary

0.0

Posted On

16 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, TensorFlow, PyTorch, Scikit-learn, Deep Learning, NLP, Ensemble Methods, LLM Fine-tuning, RAG Systems, Prompt Optimization, AWS, GCP, Docker, Kubernetes, ML Ops

Industry

technology;Information and Media

Description

dunnhumby is the global leader in Customer Data Science, partnering with the world’s most ambitious retailers and brands to put the customer at the heart of every decision. We combine deep insight, advanced technology, and close collaboration to help our clients grow, innovate, and deliver measurable value for their customers. dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Nestlé, Unilever and Metro. We’re looking for Senior AI engineer who expects more from their career. It’s a chance to apply your expertise to distil complex problems into compelling insights using the best of data analytics and human creativity to deliver effective and impactful solutions for clients. Joining our advanced data science team, you’ll investigate, develop, implement and deploy a range of complex applications and components while working alongside super-smart colleagues challenging and rewriting the rules, not just following them. You will Design and implement scalable AI , GenAI and Machine Learning solutions that deliver measurable business impact. This role focuses on bridging Data Science and Production Engineering—ensuring models are production-ready, performant, and integrated seamlessly into enterprise systems. What we expect from you Experience: 5+ years in AI/ML engineering with hands-on experience in deploying models at scale. Core Skills: Strong proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Specialization: Expertise in at least one area—Deep Learning, NLP, or Ensemble Methods. GenAI/LLM: Practical experience with LLM fine-tuning, RAG systems, and prompt optimization. Engineering Stack: Familiarity with cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), and ML Ops tools. Software Engineering: Solid understanding of software design principles, testing, and CI/CD pipelines. What you can expect from us We won’t just meet your expectations. We’ll defy them. So you’ll enjoy the comprehensive rewards package you’d expect from a leading technology company. But also, a degree of personal flexibility you might not expect. Plus, thoughtful perks, like flexible working hours and your birthday off. You’ll also benefit from an investment in cutting-edge technology that reflects our global ambition. But with a nimble, small-business feel that gives you the freedom to play, experiment and learn. And we don’t just talk about diversity and inclusion. We live it every day – with thriving networks including dh Gender Equality Network, dh Proud, dh Family, dh One, dh Enabled and dh Thrive as the living proof. We want everyone to have the opportunity to shine and perform at your best throughout our recruitment process. Please let us know how we can make this process work best for you. Our approach to Flexible Working At dunnhumby, we value and respect difference and are committed to building an inclusive culture by creating an environment where you can balance a successful career with your commitments and interests outside of work. We believe that you will do your best at work if you have a work / life balance. Some roles lend themselves to flexible options more than others, so if this is important to you please raise this with your recruiter, as we are open to discussing agile working opportunities during the hiring process. For further information about how we collect and use your personal information please see our Privacy Notice which can be found (here)

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Responsibilities

The role involves investigating, developing, implementing, and deploying complex AI/ML applications and components, focusing on bridging Data Science and Production Engineering. Responsibilities include designing and implementing scalable AI, GenAI, and Machine Learning solutions that deliver measurable business impact.