Company Description
Entain India is the engineering and delivery powerhouse for Entain, one of the world's leading global sports and gaming groups. Established in Hyderabad in 2001, we've grown from a small tech hub into a dynamic force, delivering cutting-edge software solutions and support services that power billions of transactions for millions of users worldwide.
Our focus on quality at scale drives us to create innovative technology that supports Entain's mission to lead the change in global sports and gaming sector. At Entain India, we make the impossible possible, together.
About the Role
We are looking for a hands-on, cloud-savvy Machine Learning Engineer with 2+ years of experience to join our Data Science & AI Center of Excellence (CoE).
In this role, you will help design, operationalize, and scale production-grade machine learning systems that power real-time, high-availability betting and gaming experiences.
You will work closely with Data Scientists, ML Engineers, Cloud Engineers, and Product teams to build end-to-end ML pipelines, automate deployment workflows, and ensure models run efficiently, reliably, and securely in production environments.
This position is ideal for someone who is passionate about MLOps, automation, cloud-native ML tooling, and scalable model governance.
Key Responsibilities
MLOps & Productionization
Build and maintain automated ML pipelines for training, testing, deployment, and monitoring of models.
Deploy ML models to production using modern MLOps frameworks, CI/CD pipelines, containerization, and orchestration systems.
Implement robust model versioning, lineage, reproducibility, and governance practices.
Model Monitoring & Reliability
Develop monitoring dashboards and alerts for model performance, data drift, feature quality, and service health.
Ensure uptime, scalability, and resilience of ML services in real-time, high-traffic environments.
Data Engineering & Feature Delivery
Collaborate with Data Engineering to build high-quality feature pipelines, implement feature stores, and optimize data flows for training and inference.
Build data validation checks, data quality alerts, and schema enforcement.
Cloud & Infrastructure
Design cloud-native ML workflows using AWS/GCP services (depending on company stack).
Optimize resource usage, cost efficiency, and infrastructure automation for ML workloads.
Collaboration & Best Practices
Work with the CoE team to establish MLOps best practices, templates, reusable components, and internal frameworks.
Partner with Data Science teams to accelerate model development lifecycle while ensuring compliance and security.
Contribute to technical documentation, design reviews, and knowledge-sharing initiatives.
Qualifications
Technical Experience
2+ years of hands-on experience in Machine Learning Engineering or MLOps roles.
Strong proficiency in Python and ML/DS libraries (e.g., scikit-learn, pandas, NumPy).
Experience building and deploying ML models into production.
Practical experience with containerization (Docker) and orchestration (e.g., airflow, prefect, etc).
Experience with CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins, etc.).
Understanding of ML lifecycle management: experiment tracking, feature engineering, model validation, monitoring, retraining.
Cloud & DevOps
Hands-on experience with cloud platforms (AWS, GCP) and ML-native cloud services.
Familiarity with infrastructure-as-code tools (Terraform, CloudFormation, etc.).
Soft Skills
Strong problem-solving abilities and ownership mindset.
Ability to work in a fast-paced, highly collaborative environment.
Clear communication skills with both technical and non-technical stakeholders.
Additional Information
We know that signing top players requires a great starting package, and plenty of support to inspire peak performance. Join us, and a competitive salary is just the beginning.
Depending on your role and location, you can expect to receive benefits like:
MNCJobsIndia.com will not be responsible for any payment made to a third-party. All Terms of Use are applicable.