Senior Machine Learning Ops Engineer

We are...

An innovative company committed to redefining sports media, daily fantasy, and sports wagering for fans worldwide. As a leader in the iGaming media field, we focus on ensuring a transparent and secure space for sports betting through advanced technologies and expert insights. We offer a dynamic environment for sports enthusiasts and teamwork lovers alike, where your passion can shine and make work both meaningful and enjoyable. With a focus on collaboration, fun, and inclusivity, we invite you to join us on this exciting journey!

You are…

An experienced MLOps Engineer who thrives on building the backbone of production-grade ML systems. You enjoy bridging the gap between model development and production, creating scalable infrastructure, and empowering ML teams to ship models with confidence. You’re comfortable working across cloud services, containerized environments, and CI/CD pipelines—and you understand the importance of reproducibility, monitoring, and automation.

You will…

• Design and implement infrastructure for ML model training, testing, deployment, and monitoring.

• Collaborate with ML engineers and data scientists to streamline model operationalization and CI/CD integration.

• Manage containerized environments (Docker, Kubernetes/ECS) and model serving infrastructure.

• Monitor production ML systems for performance degradation, data drift, and retraining needs.

• Use tools like MLflow to track experiments, manage model versions, and support model governance.

• Automate workflows using tools like Airflow, Step Functions, or similar orchestration platforms.

• Contribute to IaC (e.g., Terraform or CloudFormation) to ensure reproducible infrastructure deployments.

Hiring Model: PJ

Requirements

Must have:

• A degree in computer science, engineering, or a related field.

• 3–6+ years of experience in DevOps, Cloud Engineering, or MLOps roles supporting machine learning teams.

• Proficiency in Python and experience with ML frameworks (e.g. Scikit-learn, PyTorch, TensorFlow).

• Hands-on experience with AWS services like S3, SageMaker, Lambda, Redshift, and ECR.

• Expertise with containerization and orchestration tools (Docker, ECS, Kubernetes).

• Familiarity with ML lifecycle tooling such as MLflow, DVC, or SageMaker Pipelines.

• Solid understanding of CI/CD pipelines, Git workflows, and infrastructure-as-code.

• Strong collaboration skills and a mindset of continuous improvement.

• Fluency in English.

Nice to have:

• Experience with real-time model serving and streaming pipelines.

• Knowledge of observability frameworks (Prometheus, Grafana, etc.).

• Familiarity with security and compliance practices for ML systems.

• Interest in emerging areas such as ML governance or responsible AI.