Generative AI Engineer (Product Intelligence)

Opis oferty

About the role

The Product Intelligence Platform is a generative AI-driven system that transforms how Tesco handles product data at scale. The platform leverages multi-agent generative AI systems to automate product categorisation, enrichment and matching across a vast number of products every week, processing over 1 million events weekly on an event-driven Kafka architecture.

This role sits closest to our generative AI work: prompt engineering, LLM orchestration, embeddings, scoring and evaluation. You will work alongside our Java/Spring Boot platform engineers but will spend most of your time in Python, on the services and pipelines that sit nearest to the models.

A lot of this work starts as ambiguous, research-shaped problems — “is this even feasible?” — before becoming production services. You'll run feasibility studies and proofs of concept, then carry the strongest ideas through to production, working closely with product managers, data stewards and engineering peers along the way.

What is in it for you

We know life looks a little different for each of us. That’s why at Tesco, we always welcome chats about different flexible working options. Some people are at the start of their careers, some want the freedom to do the things they love. Others are going through life-changing moments like becoming a carer, adapting to parenthood, or something else. So, talk to us throughout your application about how we can support. This role requires you to be based in or near Kraków, as you will spend 60% (3 days) of your week collaborating with colleagues at our office locations or local sites and the rest remotely.

Our benefits
Tesco is a diverse and exciting employer, dedicated to being #aplacetogeton, providing career-defining opportunities to all of our colleagues. If you choose to join our business, we will provide you with:

• Up to 20% yearly salary bonus – based on both individual and business performance
• Permanent contract from the go – as a sign of our trust in your abilities
• Tax-deductible expenses (IP)
• Private healthcare (LuxMed)
• Cafeteria & Multisport
• Additional 4 days of paid leave to support your well-being and family life
• Increase in annual leave (from 20 to 25 days) for those not yet eligible for the full holiday entitlement
• Learning opportunities – certified technical training and a learning platform (Udemy)
• Back-to-school day – 1 day off for parents of primary school children
• Fertility leave – up to 2 weeks of paid leave for IVF treatment
• Sports activities with a personal trainer in the office
• Referral bonus
• MacBook as your work tool
• PetHelp – support and care for your pets

If that sounds exciting, then we'd love to hear from you.

About us

Our colleagues work with one goal in mind, helping to make every day a little better for our customers, colleagues and communities all over the world. No two customers are the same, neither are our colleagues.
At Tesco, we champion a balance that lets you thrive both in and out of work. Spend 60% of your week collaborating with colleagues at our office locations or local sites and the rest remotely. Whether you're just kicking off your career, juggling passions, or navigating big life events, we're here to support you.
Tesco is committed to celebrating diversity and everyone is welcome at Tesco. As a Disability Confident Employer, we’re committed to providing a fully inclusive and accessible recruitment process, allowing candidates the opportunity to thrive and inform us of any reasonable adjustments they may require.

What our colleagues like the most at Tesco:

• We develop our own products
• We make an impact; large scale of operation
• Accountability and respect are given to us
• We cooperate and support each other
• There are great colleagues who are divided into small teams here
• We can develop and learn new things

Wymagania

Mandatory skills:

• Solid, recent experience building production backend services in Python with a modern framework (FastAPI, Django, Flask or similar).
• Practical, hands-on experience with LLMs and prompt engineering in a production or near-production setting, not only experimentation.
• Experience with embeddings and vector similarity (pgvector or an equivalent vector database).
• Good understanding of event-driven architectures and Apache Kafka.
• Comfort working on ambiguous, research-shaped problems — running feasibility studies or PoCs — and then carrying them through to a shipped solution.
• Experience with cloud platforms, preferably Azure.
• Understanding of software engineering best practices, including CI/CD, testing and code review.
• Strong stakeholder communication skills: able to navigate ambiguity and translate it into clear requirements and implementation plans.

Nice-to-have skills:

• Experience with LLM observability and evaluation tooling (Langfuse or similar).
• Experience working with multi-agent systems.
• Familiarity with Java/Spring Boot, since the wider platform is polyglot Java/Python.
• Experience with Kubernetes (Azure Kubernetes Service) for container orchestration.
• Experience with Infrastructure as Code (Terraform).
• Knowledge of the retail or e-commerce domain.
• Understanding of data quality and governance principles.
• Experience mentoring or upskilling others in applied generative AI, or involvement in hiring.

Unsure if you fit all the criteria? Apply and give us the chance to evaluate your potential – you could be the perfect fit!

Zakres obowiązków

• Leading generative AI feasibility studies and proofs of concept for new product intelligence capabilities (for example, image-based accuracy scoring or dynamic attribute handling), and turning promising results into production designs.
• Designing and building generative AI solutions that integrate large language models (Azure OpenAI GPT-4) through our GenAI Service facade, including prompt design, evaluation and iteration.
• Building and maintaining embedding-based similarity and scoring pipelines using PostgreSQL with pgvector.
• Contributing to LLM observability and evaluation practices (e.g. with Langfuse), including regression testing and drift detection as prompts and models change.
• Supporting Human-in-the-Loop (HITL) tooling that helps data stewards review and validate generative AI output efficiently — 100% HITL review before publication is a core control on this platform.
• Contributing to responsible Gen AI governance activities, including audit support and structured release criteria for prompt and model changes.
• Building event-driven services in Python for high-volume data processing on Kafka.
• Collaborating with product managers, data stewards and other engineers to translate ambiguous business problems into clear technical designs.
• Sharing generative AI knowledge with the wider team and contributing to upskilling.
• Optionally representing the team's generative AI work externally, such as at conferences or in the tech community.