MLOps Engineer

Job Description:

• We are looking for a skilled MLOps Engineer who possesses extensive experience in deploying, managing, and optimizing machine learning workloads within production settings.
• This position emphasizes MLOps (60%), complemented by AWS Cloud (25%) and DevOps (15%) capabilities.
• The successful candidate will take hands-on ownership of the complete ML lifecycle, which includes model training, deployment, monitoring, automation, performance optimization, and retraining.
• A strong background in AWS services, CI/CD pipelines, containerization, infrastructure automation, and production-grade ML platforms is crucial.
• This role is specifically tailored for MLOps; candidates must showcase a proven track record in operationalizing and sustaining ML models at scale within cloud environments.

Key Responsibilities:

• 5+ years of experience in DevOps, Cloud, and MLOps with proficiency in Python for scripting and automation.
• Expertise in Jenkins, Git, Docker, and EKS troubleshooting.
• Experience with AWS services such as SageMaker, Lambda, S3, ECS, IAM, RDS, and infrastructure creation.
• Familiarity with Infrastructure as Code (IaC) using CloudFormation, CFT, and Terraform.
• Responsibilities include building and operating ML pipelines, deploying to SageMaker, Databricks, or Lambda.

Technical Requirements:

• SageMaker
• MLflow
• Kubeflow
• Databricks
• MLOps
• Model Deployment
• Model Monitoring
• Model Retraining
• Feature Store
• CI/CD for ML
• Training Pipelines
• Inference Pipelines
• Drift Detection
• Docker
• Kubernetes EKS
• Terraform
• CloudFormation
• AWS Lambda
• Python Automation

Preferred Skills:

Technology->AI-AI Engineering->MLOps, Technology->AI-Data Science->Machine Learning, Technology->DevOps->Ansible, Technology->Open System->Python - Open System