Software Engineer III at Stats Perform

Prague, Prague, Czechia -

Full Time

Start Date

Immediate

Expiry Date

20 Sep, 26

Salary

0.0

Posted On

22 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, AWS, Kubernetes, Terraform, MLOps, LLMOps, PyTorch, Scikit-learn, Airflow, CI/CD, FastAPI, Generative AI

Industry

Spectator Sports

Description

Overview Stats Perform is the market leader in sports tech. We provide the most trusted sports data to some of the world's biggest organizations, across sports, media, and broadcasting. Through the latest AI technologies and machine learning, we combine decades' worth of data with the latest in-game happenings. We then offer coaches, teams, professional bodies, and media channels around the world, access to the very best data, content, and insights. In turn, improving how sports fans interact with their favourite sports teams and competitions. How do we add value? Media outlets add a little magic to their coverage with our stats and graphics packages. Sportsbooks can offer better predictions and more accurate odds. The world's top coaches are known to use our data to make critical team decisions. Sports commentators can engage with fans on a deeper level, using our stories and insights. Anywhere you find sport, Stats Perform is there. However, data and tech are only half of the package. We need great people to fuel the engine. We succeeded thanks to a team of amazing people. They spend their days collecting, analyzing, and interpreting data from a wide range of live sporting events. If you combine this real-time data with our 40-year-old archives, elite journalists, camera operators, copywriters, the latest in AI wizardry, and a host of 'behind the scenes' support staff, you've got all the ingredients to make it a magical experience! Responsibilities: · Developing and deploying tools and services for training and inference of machine learning models using Python, AWS, Kubernetes, and Terraform. · Working closely with AI/DS teams to productionize new ML models, ensuring seamless transition from research to production. · Driving improvements in ML lifecycle management using MLOps and LLMOps best practices. · Ensuring seamless real-time model serving for high-performance AI applications across multiple sports. · Monitoring infrastructure costs, resource usage, and system health — proactively identifying inefficiencies, reacting to anomalies, and planning optimisation actions to ensure sustainable and cost-effective platform operations. · Exploring and applying the latest tools for working with LLMs and Generative AI models. · Providing technical guidance to various teams on MLOps approaches, deployment strategies, and platform capabilities. · Supporting, training, and mentoring team members on ML infrastructure and deployment best practices. Required Qualifications: · 4+ years of commercial experience in software engineering, including at least 2 years in ML Engineering roles such as MLOps Engineer, ML Platform Engineer, or similar infrastructure-focused positions. · Expert proficiency in Python and strong experience with cloud deployment on AWS. · Hands-on experience with Kubernetes for orchestrating ML workloads at scale. · Experience scaling cloud and on-premise architectures, including real-time ingestion, training, deployment, and monitoring of ML models. · Familiarity with MLOps frameworks (MLflow, Ray, Kubeflow) and modern data pipeline orchestration (Airflow). · Familiarity with ML frameworks such as PyTorch, Scikit-learn, and others. · Understanding of CI/CD pipelines (Jenkins, GitHub Actions) and infrastructure-as-code (Terraform). · Strong problem-solving skills and ability to collaborate effectively with cross-functional teams. · Fluency in English; good verbal and written communication skills, including an ability to effectively communicate with both technical and non-technical stakeholders. Desired Qualifications: · Knowledge of model optimization techniques (quantization, pruning) for efficient model serving. · Hands-on experience with GPU infrastructure for training large-scale models and model inference. · Experience with GenAI models (LLMs) and open-source tools such as LangChain, vLLM, or HuggingFace. · Familiarity with monitoring and observability tools like Prometheus or Grafana. · Experience with FastAPI or similar frameworks for building model serving APIs. · Experience with sports event and/or tracking data and deep interest in sports. · Curiosity and enthusiasm for working with next-gen AI technologies in a sports data context. Why work at Stats Perform? We love sports, but we love diverse thinking more! We know that diversity brings creativity, so we invite people from all backgrounds to join us. At Stats Perform you can make a difference, by using your skills and experience every day, you'll feel valued and respected for your contribution. We take care of our colleagues We like happy and healthy colleagues. You will benefit from things like Mental Health Days Off, ‘No Meeting Fridays,’ and flexible working schedules. We pull together to build a better workplace and world for all. We encourage employees to take part in charitable activities, utilize their 2 days of Volunteering Time Off, support our environmental efforts, and be actively involved in Employee Resource Groups. Diversity, Equity, and Inclusion at Stats Perform By joining Stats Perform, you'll be part of a team that celebrates diversity. A team that is dedicated to creating an inclusive atmosphere where everyone feels valued and welcome. All employees are collectively responsible for developing and maintaining an inclusive environment. That is why our Diversity, Equity, and Inclusion goals underpin our core values. With increased diversity comes increased innovation and creativity. Ensuring we're best placed to serve our clients and communities. Stats Perform is committed to seeking diversity, equity, and inclusion in all we do.

Responsibilities

Develop and deploy tools for machine learning model training and inference using Python, AWS, and Kubernetes. Drive improvements in ML lifecycle management and ensure high-performance real-time model serving for sports applications.