MLOps Engineer (Python + K8s / AWS)
Automated Insights
Job 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 favorite sports teams and competitions.
How do they use it?
- 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!
Our teams of technical experts specialize in harnessing live sporting data, using advanced cloud technologies, Java, JavaScript and Python. It is these tech teams that enable us to extract patterns through AI and Machine Learning and deliver our insights via APIs. In short, they turn complex data into magical experiences with cutting-edge technology.
What you’ll be doing…
We are looking for a MLOps Engineer (Python + K8s and AWS) for our ML Platform team to help build services, tools and processes to accelerate classic ML and Generative AI models delivery. ML Platform team is maintaining various components of the machine learning services within an organization. Its primary focus revolves around three key areas:
- model registry and experiments tracking,
- real-time model serving
- also LLMs and other Generative AI models
- cloud training infrastructure.
ML Platform provides full lifecycle management for dozens of ML models across various sports (Soccer: Live Win Probability, Season Simulation, Player and Team props, xG, PV/Momentum, Power Rankings, Opta Vision; Cricket, Basketball, American Football, Tennis: Live Win Probability, Season Simulation; …).
As a experienced/senior engineer you will be expected to work independently, help solve technical challenges, and be an example in following best practices.
You will…
- Develop and deploy tools and services for machine learning inference (real-time) and training (Python, AWS, Terraform, CI/CD Jenkins, Kubernetes, MLFlow).
- Work together with AI/DS team to help train and productionize new ML models.
- Drive changes to ML project model lifecycle according to MLOps, LLMOps & Engineering best practices.
- Research and apply latest tools and advancements in operating open-source LLM and other Generative AI models
- Acquire expertise in sports data, including live event data, tracking data, player statistics, tournaments, and schedules for various sports.
- Refactor and optimise for production PoCs created by AI/DS team.
- Design and implement libraries, which can be used by other teams (Python).
- Use logging and monitoring tools to ensure end-to-end observability.
- Engage in technical design discussions within the engineering teams and other senior engineers in the organization.
* This role does not involve direct Data Science, Machine Learning work (ML models are designed and trained by a dedicated Predictive Modelling team)
Preferred qualifications
- Experience with Python.
- Knowledge of ML lifecycle management tools and platforms: MLFlow, Weights and Biases.
- Knowledge of MLOps/LLMOps principles
- Experience with data/feature engineering.
- Knowledge of Machine Learning.
- Knowledge of sports data domain or desire to learn it
- Experience with PyTorch.
- Experience deploying AWS Cloud Infrastructure (Terraform, CloudFormation, CDK) or any other public cloud.
- Experience with using Kubernetes for ML workloads
Nice to have
- Knowledge of Data Science.
- Experience with other ML Frameworks (Tensorflow, sklearn)
- Knowledge of ML Monitoring tools and platforms: Evidently, WhyLogs.
- Experience in building MLOps infrastructure for inference and training
- Experience with Python libraries Pandas, SciPy, NumPy, HuggingFace transformers
- Knowledge of Feature Stores
- Familiarity of challenges with deploying open-source LLMs & Generative AI models on GPUs
- Experience in developing Gen AI solutions using LLMs, LangChain/Llama Index, prompt engineering, and fine-tuning techniques.
- Knowledge about LLM and Gen AI model optimization techniques and serving tools
- quantization, pruning, compilation
- vLLM, Ollama, Triton
- Experience with solution architecture and system design.
- Experience with SQL and NoSQL databases.
- Experience with event-driven, messaging, distributed systems.
- Experience with Docker (or other containers).
- Experience with generic logging and monitoring frameworks (ELK, Prometheus, Grafana).
- Experience with AWS streaming services, e.g. MSK (Kafka), Kinesis.
- Experience with AWS microservices capabilities, e.g. Lambda, SNS, SQS.
- Experience with AWS data services, e.g. DynamoDB, S3, Redshift.
General requirements
- Bachelor’s degree in Computer Science or similar
- Verbal/written communication and presentation skills, including the ability to effectively communicate with both business and technical teams, and both internal and external stakeholders
- An open-minded, structured thinker, a team player and a good teammate
- Intellectual curiosity and excellent problem-solving skills, including the ability to structure and prioritize the approach for maximum impact
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.
Job Info
- Job Identification 477
- Job Category Technology
- Posting Date 10/02/2024, 03:53 AM
- Job Schedule Full time
- Job Shift Day
- Locations Boudnikova 2514/5, Liben, 180 00, Prague 8, CZ Boudnikova 2514/5, Prague, 180 00, CZ