Senior Machine Learning Operations (MLOps) Engineer

We are looking for an experienced Senior MLOps Engineer to design, build, deploy, and maintain scalable machine learning platforms and production ML systems. The ideal candidate will have strong hands-on experience in end-to-end ML lifecycle management, cloud-based ML platforms, CI/CD automation, model deployment, and production monitoring.

Key Responsibilities:

• Design, build, and maintain scalable production-grade machine learning platforms.
• Develop automated CI/CD/CT pipelines for model training, validation, deployment, and retraining.
• Implement model versioning, experiment tracking, model registries, and feature stores.
• Deploy and manage batch and real-time machine learning inference workloads.
• Build scalable data pipelines integrating enterprise data platforms and cloud storage.
• Monitor model performance, data drift, feature drift, and production reliability.
• Implement infrastructure automation, model governance, security, and compliance controls.
• Collaborate with data scientists, data engineers, and software engineering teams to deliver reliable ML solutions.

Required Skills:

• 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
• At least 3 years of hands-on experience supporting production machine learning systems.
• Strong programming skills in Python and SQL.
• Experience managing the end-to-end ML lifecycle, including deployment and monitoring.
• Hands-on experience with AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Experience building CI/CD pipelines, model deployment workflows, and automated retraining processes.
• Experience with model versioning, experiment tracking, and ML pipeline development.
• Familiarity with Docker, Kubernetes, Git, Jenkins, GitHub Actions, or Azure DevOps.
• Experience working with enterprise data platforms such as Snowflake, Databricks, or equivalent technologies.

Preferred Qualifications:

• Experience with MLflow, Kubeflow, SageMaker Pipelines, Airflow, or Feast.
• Knowledge of feature stores, model registries, and ML governance frameworks.
• Experience with Apache Kafka, streaming architectures, and real-time inference.
• Familiarity with Terraform, Infrastructure as Code, Prometheus, and Grafana.
• Banking, financial services, fraud detection, or credit risk experience is a plus.