About the role
Role Overview
We are seeking an experienced MLOps Engineer to design, build, and maintain the infrastructure required to scale our artificial intelligence and machine learning initiatives. In this role, you will collaborate closely with Data Scientists, DevOps team, and Backend Engineers to transition ML prototypes into robust, high-performance production systems.
Key Responsibilities
- Build and manage end-to-end ML pipelines (ingestion, feature engineering, training, validation) using SageMaker Pipelines and on-prem tools (Airflow/Kubeflow)
- Implement CI/CD pipelines for ML lifecycle
- Deploy models using SageMaker and on-prem Kubernetes/microservices architecture
- Monitor model performance, data drift, and system health
- Manage scalable infrastructure (CPU/GPU) for training and low-latency inference
- Ensure model/data versioning, lineage tracking, and governance compliance
- Convert experimental ML code into production-ready, modular systems
Required Skills
- Strong experience in AWS (SageMaker, S3, EC2, IAM, CloudWatch)
- Hands-on with Python + ML frameworks (PyTorch, Scikit-learn, TensorFlow)
- Experience with Docker, Kubernetes (on-prem deployment)
- Knowledge of MLOps tools (MLflow/Kubeflow, DVC, Airflow/Prefect)
- CI/CD tools (Jenkins, GitHub Actions, GitLab CI) and IaC (Terraform/CloudFormation)
- Understanding of SQL/NoSQL and data pipelines
Experience & Qualifications
- 3–6 years in MLOps, DevOps, or Data Engineering
- Bachelor’s/Master’s in relevant field
- Experience with production ML systems under SLA (latency/availability)
Good to Have
- Hybrid (cloud + on-prem) architecture experience
- Model monitoring, feature store knowledge
- AWS or ML certifications