onsite
MLOps Engineer
MLOps Engineer
MindWise Technologies is hiring an MLOps Engineer to build, deploy, monitor, and scale machine learning models in production. This role involves managing ML pipelines, automating CI/CD workflows, and ensuring the reliability and security of ML systems in collaboration with Data Scientists and Product teams.
About the role
Role Overview
We are looking for an experienced MLOps Engineer to build, deploy, monitor, and scale machine learning models in production. This role works closely with Data Scientists, Backend Engineers, and Product teams to ensure reliable and secure ML systems.
Key Responsibilities
- Deploy, manage, and maintain ML models in production environments
- Design and manage ML pipelines for training, testing, and deployment
- Automate CI/CD workflows for ML models and data pipelines
- Monitor model performance, data drift, and system reliability
- Manage cloud infrastructure and containerized environments
- Ensure model versioning, reproducibility, and rollback strategies
- Collaborate with Data Science teams to productionize models
- Implement best practices for security, scalability, and cost optimization
Required Skills & Experience
- Minimum 3 years of experience in MLOps, ML Engineering, or DevOps with ML systems
- Strong experience with Python
- Hands-on experience with Docker and Kubernetes
- Experience with cloud platforms (AWS / GCP / Azure)
- Knowledge of ML lifecycle management tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.)
- Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, etc.)
- Understanding of monitoring, logging, and alerting systems
- Strong understanding of model deployment, scaling, and performance monitoring
- Experience with container orchestrators for deployment, such as AWS ECS/EKS
- Experience with monitoring and logging tools like Prometheus, Grafana, CloudWatch, Elasticsearch
Good to Have
- Experience with data pipelines (Airflow, Prefect, or similar)
- Knowledge of feature stores and data versioning tools
- Exposure to AI/ML applications in production environments
- Experience working with large datasets and real-time inference
- Familiarity with data lakes (S3,Glue,Iceberg, Athena/Spark)