MLOps Engineer

What We Need

The MLOps Engineer is responsible for designing, implementing, and managing the end-to-end lifecycle of 2K’s machine learning models. Beyond building individual models, this role focuses on creating the automated systems that facilitate training, deployment, monitoring, and retraining  an advanced professional, the MLOps Engineer ensures that ML infrastructure is robust, performant, and capable of adapting to evolving player behavior, providing the heavy lifting required for seamless ML deployment across 2K’s global titles.

• ML Lifecycle Automation: Lead the design and maintenance of end-to-end ML pipelines covering data ingestion, feature engineering, model training, and deployment.
• Model Serving Architecture: Architect and manage scalable model serving infrastructure (API-based or streaming) utilizing tools such as Databricks Model Serving, Seldon, or Sage Maker.
• Feature Store Management: Implement and maintain a centralized feature store to ensure consistency and low-latency access between training and real-time inference.
• CI/CD for Machine Learning (CT): Develop and optimize Continuous Training (CT) pipelines that automate model retraining based on performance decay or new data availability.
• Monitoring & Observability: Implement specialized monitoring for ML assets, tracking model drift, feature skew, and prediction latency to ensure high-quality player experiences.
• Cross-Functional Collaboration: Partner with Data Scientists to refactor experimental code into production-ready, modular, and testable components.
• Resource & Cost Optimization: Manage and optimize the infrastructure costs of GPU/CPU clusters, ensuring training jobs are balanced for performance and budget efficiency.

• Technical Leadership: Acts as a subject matter expert in ML infrastructure, guiding the technical direction of model deployment strategies.
• Operational Excellence: Focuses on building highly reliable and performant automated ML systems.
• Problem Solving: Diagnoses and resolves complex issues within non-deterministic ML code and distributed infrastructure.
• Influence &
  
  Collaboration:
  
  Mentors Data Scientists on software engineering best practices while effectively communicating infrastructure constraints to stakeholders.
• Agility: Quickly adapts ML strategies to support massive live-service ecosystems and changing data landscapes.

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.
• Experience: 6+ years of professional experience in MLOps, Dev Ops, or ML Engineering (with a focus on product ionizing ML at scale).
• ML Stack Mastery: Deep experience with Databricks/MLflow, Kubeflow, or AWS Sage Maker.
• Technical Proficiency: Expert-level Python (including ML libraries like Scikit-Learn, PyTorch, or Tensor Flow) and advanced SQL.
• Big Data Engineering: Proficient in using Spark/PySpark to process massive datasets for feature extraction and engineering.
• Orchestration: Hands-on experience with Airflow or specialized ML orchestrators to manage complex dependency graphs.
• Deployment & Infrastructure: Strong understanding of Docker, Kubernetes, and CI/CD principles applied to ML life cycles.

• Experience with real-time inference for high-concurrency applications (e.g., in-game personalization).
• Familiarity with Data Privacy regulations (GDPR/CCPA) as they relate to model training and PII.
• Knowledge of Reinforcement Learning or Recommendation Systems in a gaming context.

• Great Company Culture
  . We pride ourselves as being one of the most creative and innovative places to work. Creativity, innovation, efficiency, diversity and philanthropy are among the core tenets of our organization and are integral drivers of our continued success.
• Growth
  :
  As a global entertainment company, we pride ourselves on creating environments where employees are encouraged to be themselves, inquisitive, collaborative and to grow within and around the company.
• Work Hard, Enjoy Life. Our employees bond, blow-off steam, and flex some creative muscles through corporate boot…