ML Ops Engineer

We are looking for an experienced MLOps Engineer to design, build, deploy, and maintain scalable machine learning operations pipelines on AWS. The ideal candidate will work closely with data scientists, machine learning engineers, data engineers, and DevOps teams to productionize AI/ML models, automate deployment workflows, and ensure models are reliable, scalable, secure, and well-monitored in production environments.

Key Responsibilities:

Design, build, and maintain end-to-end MLOps pipelines for model training, validation, deployment, monitoring, and retraining.

Develop and automate CI/CD pipelines for machine learning models and related services using tools such as AWS CodePipeline, AWS CodeBuild, Jenkins, GitLab CI/CD, or GitHub Actions.

Deploy, manage, and monitor machine learning models on AWS using services such as Amazon SageMaker, AWS Lambda, Amazon ECS, Amazon EKS, and API Gateway.

Build scalable model serving solutions for batch, real-time, and event-driven inference use cases.

Implement model versioning, experiment tracking, artifact management, and reproducibility using tools such as Amazon SageMaker Model Registry, MLflow, or similar platforms.

Containerize ML applications and services using Docker and deploy them using Kubernetes, Amazon EKS, or Amazon ECS.

Collaborate with data scientists and AI/ML engineers to move machine learning models from development to production.

Monitor production models for performance, accuracy, latency, data drift, model drift, and system reliability.

Build automation for model retraining, validation, approval workflows, and production deployment.

Work with AWS data and storage services such as Amazon S3, Amazon Redshift, AWS Glue, Amazon Athena, Amazon RDS, and DynamoDB as needed.

Implement infrastructure as code using Terraform, AWS CloudFormation, or AWS CDK.

Ensure security, access control, compliance, and governance using AWS IAM, VPC, CloudWatch, CloudTrail, KMS, and related AWS services.

Troubleshoot and resolve issues related to ML pipelines, cloud infrastructure, deployments, data pipelines, and production model performance.

Document MLOps processes, deployment standards, monitoring practices, and operational runbooks.

Required Skills and Qualifications:

Bachelor’s degree in Computer Science, 

Engineering, Data Science, Information Technology, or a related field.

Strong experience in MLOps, DevOps, machine learning engineering, cloud engineering, or platform engineering.

Hands-on experience with AWS cloud services, especially Amazon SageMaker, S3, Lambda, ECS, EKS, IAM, CloudWatch, and related services.

Strong programming experience with Python.

Experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, or similar.

Hands-on experience building CI/CD pipelines and automating deployment workflows.

Strong knowledge of Docker, containerization, and container orchestration using Kubernetes, Amazon EKS, or Amazon ECS.

Experience with model deployment patterns, including real-time inference, batch inference, and API-based model serving.

Familiarity with ML lifecycle tools such as SageMaker Pipelines, SageMaker Model Registry, MLflow, Kubeflow, or DVC.

Experience with infrastructure as code tools such as Terraform, CloudFormation, or AWS CDK.

Good understanding of model monitoring, data drift, model drift, logging, alerting, and production support.

Knowledge of version control tools such as Git.

Strong troubleshooting, analytical, communication, and collaboration skills.

Preferred Qualifications:

AWS certification such as AWS Certified Machine Learning – Specialty, AWS Certified Solutions Architect, or AWS Certified DevOps Engineer.

Experience with data engineering tools such as AWS Glue, Apache Spark, Airflow, Kafka, or Databricks.

Experience with feature stores, model registries, automated retraining pipelines, and model governance.

Understanding of security best practices for cloud-based ML environments.

Experience working in Agile/Scrum development environments.