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MLOps Engineer – Sports Analytics and Performance Intelligence (NBA)
JOB SUMMARY
Job details
This is a remote position.
We are looking for an MLOps Engineer to support a sports analytics and performance intelligence platform used by professional basketball organisations, including NBA teams.
The platform processes large volumes of historical and near real time player, workload, and game event data. Machine learning models generate insights consumed by analysts, coaches, and front office teams, while LangChain and LangGraph based GenAI components orchestrate comparisons, narratives, and interactive exploration.
The environment prioritises speed, scalability, and practical impact in live analytical workflows. ResponsibilitiesDeploy and operate machine learning models supporting sports analytics use casesBuild and maintain pipelines for model training, validation, and deploymentSupport high frequency and time series data processing workflowsDeploy and manage LangChain and LangGraph based GenAI servicesImplement monitoring for model performance, drift, and data qualityEnsure reproducibility and version control for models and datasetsSupport fast iteration and experimentation in production-like environmentsCollaborate closely with data scientists and engineers to streamline model lifecycle
Requirements
Strong experience in MLOps or ML platform engineeringSolid Python skills for automation and toolingHands on experience with Docker and KubernetesExperience with MLflow or similar model lifecycle management toolsExperience building CI/CD pipelines for ML workloadsExperience working with high volume or time series datasetsBasic experience with monitoring and observability toolingFluent English for collaboration in an international teamNice to haveExperience deploying LangChain or LangGraph based servicesBackground in sports analytics or performance dataExperience working in fast paced product environmentsFamiliarity with real time or streaming data architectures
Benefits
Solid, competitive salaryWork in a multinational environment on international projectsComprehensive healthcareLong-term B2B contract with a stable project pipelineRemote work model.
We are looking for an MLOps Engineer to support a sports analytics and performance intelligence platform used by professional basketball organisations, including NBA teams.
The platform processes large volumes of historical and near real time player, workload, and game event data. Machine learning models generate insights consumed by analysts, coaches, and front office teams, while LangChain and LangGraph based GenAI components orchestrate comparisons, narratives, and interactive exploration.
The environment prioritises speed, scalability, and practical impact in live analytical workflows. ResponsibilitiesDeploy and operate machine learning models supporting sports analytics use casesBuild and maintain pipelines for model training, validation, and deploymentSupport high frequency and time series data processing workflowsDeploy and manage LangChain and LangGraph based GenAI servicesImplement monitoring for model performance, drift, and data qualityEnsure reproducibility and version control for models and datasetsSupport fast iteration and experimentation in production-like environmentsCollaborate closely with data scientists and engineers to streamline model lifecycle
Requirements
Strong experience in MLOps or ML platform engineeringSolid Python skills for automation and toolingHands on experience with Docker and KubernetesExperience with MLflow or similar model lifecycle management toolsExperience building CI/CD pipelines for ML workloadsExperience working with high volume or time series datasetsBasic experience with monitoring and observability toolingFluent English for collaboration in an international teamNice to haveExperience deploying LangChain or LangGraph based servicesBackground in sports analytics or performance dataExperience working in fast paced product environmentsFamiliarity with real time or streaming data architectures
Benefits
Solid, competitive salaryWork in a multinational environment on international projectsComprehensive healthcareLong-term B2B contract with a stable project pipelineRemote work model.
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Madiff United States
