We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on our know-how and technology to boost their business.

ABOUT US:

Sportradar is a sports data and technology company. As Forward Deployed AI Engineer, you work like an internal consultant: you are placed in a department or business unit, talk with the people there, find the real problems, cut through complexity, simplify how work is done, and then build and ship LLM and agent-based solutions. You work largely independently with little day-to-day supervision, find your way through organisational complexity and competing priorities to get things done, and learn fast. Senior engineers are available for guidance.

THE CHALLENGE:

Talk with colleagues in your department to understand workflows, pain points and data.
Identify problems worth solving and break unclear or cluttered situations into clear, manageable steps.
Streamline and simplify processes, and propose where an AI solution makes sense and where it does not.
Work with several stakeholders and competing priorities, and keep your work moving pragmatically.
Build, test and deploy LLM-powered applications and agents, from prototype to production.
Implement retrieval (RAG), tool integrations, prompts and context handling.
Build and run evaluations (test sets, automated checks, quality dashboards) and use the results to improve solutions.
Build and maintain data pipelines that feed these systems.
Plan your own work, give regular updates and document solutions.

ABOUT YOU:

Around 2+ years of software engineering or applied ML experience, or equivalent project experience.
Clear communication with non-technical colleagues; able to explain technical topics simply.
Self-motivation and the ability to work independently with little supervision.
Pragmatic approach to organisational complexity and competing priorities.
Structured approach to finding problems and simplifying complexity.
Solid Python and software engineering fundamentals (testing, version control, APIs).
Hands-on experience with LLM APIs and a project using RAG or agents, including some evaluation.
Familiarity with SQL or data pipelines and with a cloud platform (AWS, Azure or GCP).

Nice to have

Consulting, internship or stakeholder-facing project experience.

Working model

Based in Warsaw. Some travel between Sportradar offices may be required.

OUR OFFER:   

A collaborative environment with colleagues from all over the world (Engineering offices in Europe, Asia and the US) including various social events and teambuilding.
Ability to shape your own workday and career via a clearly defined professional and personal development plan.
Opportunity to develop yourself within an inspiring and fast-growing company with our Career framework possibilities.
Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants.
Global Employee Assistance Programme.
Calm and Reulay app (managing stress, maintaining focus, improving sleep, building resilience).
Online training videos.
Flexible working hours.

While we appreciate the flexibility and benefits of working from home, we strongly believe that coming together in person fosters stronger connections, encourages collaboration, and drives innovation—both as individuals and as a company. The energy, shared ideas, and team support we experience in the office strengthen the foundation of our success and culture. For this reason, we are an office-first business operating on a fully on-site model, with team members working in the office five days a week to build relationships, exchange ideas, and grow together. Certain exemptions may apply depending on location — your local point of contact can provide further details.

OUR RECRUITMENT PROCESS: 

Initial Screening: A quick chat with our Talent Acquisition Partner to understand your background and expectations.
Technical Assessment: A short (home) task to showcase your technical skills.
Two Technical Interviews: Meet with the Technical team and later with the Hiring Manager to dive into your solution, and also discuss team fit.
Final Step: Receive feedback and, if successful, an offer!

At Sportradar, we celebrate our diverse group of hardworking employees. Sportradar is committed to ensuring equal access to its programs, facilities, and employment opportunities. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. We encourage you to apply even if you only meet most of the requirements (but not 100% of the listed criteria) – we believe skills evolve over time. If you’re willing to learn and grow with us, we invite you to join our team!

- Talk with colleagues in your department to understand workflows, pain points and data. - Identify problems worth solving and break unclear or cluttered situations into clear, manageable steps. - Streamline and simplify processes, and propose where an AI solution makes sense and where it does not. - Work with several stakeholders and competing priorities, and keep your work moving pragmatically. - Build, test and deploy LLM-powered applications and agents, from prototype to production. - Implement retrieval (RAG), tool integrations, prompts and context handling. - Build and run evaluations (test sets, automated checks, quality dashboards) and use the results to improve solutions. - Build and maintain data pipelines that feed these systems. - Plan your own work, give regular updates and document solutions. - Around 2+ years of software engineering or applied ML experience, or equivalent project experience. - Clear communication with non-technical colleagues; able to explain technical topics simply. - Self-motivation and the ability to work independently with little supervision. - Pragmatic approach to organisational complexity and competing priorities. - Structured approach to finding problems and simplifying complexity. - Solid Python and software engineering fundamentals (testing, version control, APIs). - Hands-on experience with LLM APIs and a project using RAG or agents, including some evaluation. - Familiarity with SQL or data pipelines and with a cloud platform (AWS, Azure or GCP).