Remote MLOps Engineer (Contract) – Python, Spark, AWS

Varite Inc.; United States; Remote; USD 16,000 - 19,000; Full time

Benefits offered by this job

Work from home

Job summary

VARITE INDIA PRIVATE LIMITED is seeking an MLOps Engineer to join the team as a contract position in Gurugram. You will design and run scalable ML pipelines on AWS, automate model inferencing, and manage IaC using Terraform, Docker, and Kubernetes. Collaboration with data scientists and engineers is essential to productionize models.

The role emphasizes Python, Spark, and cloud-native ML workflows, with an emphasis on performance, scalability, and cost efficiency in deployment.

Qualifications

• Strong proficiency in Python and Spark.
• Experience with AWS cloud services including SageMaker, Lambda, EKS, S3, Glue, EMR and Athena.
• Experience with Docker and Kubernetes for containerized apps.
• CI/CD pipelines for ML applications and infrastructure deployment.
• Terraform or similar IaC for scalable MLOps.

Responsibilities

• Design, implement, and maintain scalable MLOps pipelines on AWS.
• Automate ML model inferencing, deployment, monitoring, retraining and maintenance.
• Build Infrastructure as Code using Terraform or equivalent tools.
• Implement CI/CD pipelines for API, ML model, and infra deployments.
• Establish automated testing, versioning, and release processes for ML pipelines.
• Develop monitoring dashboards and alerting for ML workloads.
• Collaborate with data scientists and engineers to productionize models.

Skills

Python

Apache Spark

AWS

SageMaker

Terraform

Docker

Kubernetes

CI/CD

ML deployment

REST APIs

Education

Bachelor's or Master's in CS/Engineering/Data Science

Tools

Terraform

Docker

Kubernetes

SageMaker

AWS IAM & Networking

Job description

VARITE INDIA PRIVATE LIMITED is seeking an MLOps Engineer to join the team as a contract position in Gurugram. You will design and run scalable ML pipelines on AWS, automate model inferencing, and manage IaC using Terraform, Docker, and Kubernetes. Collaboration with data scientists and engineers is essential to productionize models.

The role emphasizes Python, Spark, and cloud-native ML workflows, with an emphasis on performance, scalability, and cost efficiency in deployment.