Role: MLOps Engineer
Team: Data & Analytics
Platform: Databricks on AWS
Experience: 6+ Years
Role Summary
The Client is looking for an experienced MLOps Engineer to join the Data Team and lead Machine Learning initiatives on Databricks (AWS). The role involves working closely with business stakeholders, data engineers, and analytics teams to design, deploy, and operationalize scalable ML solutions. The candidate will be responsible for building end-to-end ML pipelines, automating model deployment, and ensuring reliable and secure ML operations in production.
This role requires strong hands-on experience in Databricks, AWS, Python, MLflow, and ML lifecycle management, along with the ability to translate business requirements into production-ready ML solutions.
Key Responsibilities
- Design and implement end-to-end MLOps solutions on Databricks (AWS)
- Build and manage ML pipelines for training, testing, deployment, and monitoring
- Implement MLflow, Model Registry, and automated workflows
- Deploy and operationalize ML models into production environments
- Work closely with Business Stakeholders to identify and implement ML use cases
- Automate CI/CD pipelines for ML models
- Ensure model versioning, governance, and reproducibility
- Monitor model performance, drift, and retraining cycles
- Integrate ML solutions with enterprise data platforms and applications
- Follow best practices for security, governance, and cost optimization
- Document ML processes and operational standards
Required Technical Skills
Databricks & ML
- Databricks Machine Learning
- MLflow and Model Registry
- Delta Tables and Workflows
- Feature Engineering and ML Pipelines
- Unity Catalog (preferred)
AWS
- S3
- IAM
- EC2
- Lambda
- CloudWatch
- Step Functions (preferred)
Programming
- Python (Strong)
- PySpark
- SQL
- Machine Learning frameworks (Scikit-learn / TensorFlow / PyTorch)
DevOps & MLOps
- CI/CD pipelines
- Git
- Docker (preferred)
- Jenkins / GitHub Actions / Azure DevOps
- Model monitoring and automation
Experience Required
- 6+ years in Data Engineering / Machine Learning / MLOps
- 3+ years of hands-on experience in MLOps or ML Engineering
- Strong experience in Databricks on AWS
- Experience deploying ML models in production
- Experience working with business stakeholders
- Experience in enterprise data platforms
Soft Skills
- Strong communication and stakeholder management
- Problem-solving mindset
- Ownership and accountability
- Ability to work independently and in teams
- Strong documentation and coordination skills
Nice to Have
- Databricks Certification
- AWS Certification
- Feature Store experience
- Real-time ML deployment experience
- Infrastructure as Code (Terraform)
