MLOps Engineer

We are looking for an experienced MLOps / ML Engineer with over 4 years of experience in designing, deploying, and maintaining machine learning models in production environments. The role involves managing the full ML lifecycle, including model development, deployment, monitoring, and optimization in AWS cloud environments.

Responsibilities

Design and deploy ML pipelines using

SageMaker Pipelines

or

Kubeflow .

Automate CI/CD processes for ML using

GitHub Actions, GitLab, Jenkins, or CodePipeline .

Containerize and orchestrate ML applications using

Docker

and

Kubernetes .

Track experiments and manage model registry using

MLflow

or

SageMaker Model Registry .

Monitor models and detect drift using

SageMaker Model Monitor

or custom solutions.

Build and maintain data engineering workflows with

AWS Glue, EMR, Spark, and PySpark .

Implement infrastructure as code using

Terraform

or

AWS CloudFormation .

Apply AWS security best practices ( IAM, VPC, KMS, Secrets Manager, PrivateLink ).

Ensure observability of ML systems using

CloudWatch, Prometheus, ELK, Datadog .

Requirements

Minimum 4 years of experience in MLOps / ML Engineering.

Strong expertise in

AWS

(SageMaker, Lambda, ECR, ECS/EKS, S3, Step Functions).

Proficiency in

Python

and ML frameworks ( PyTorch, TensorFlow, Scikit-learn ).

Hands‑on experience with

containerization, Kubernetes, CI/CD, and ML pipeline orchestration .

Experience with model monitoring, experiment tracking, and drift detection.

Knowledge of

data engineering workflows

in AWS.

Experience with

Infrastructure as Code

and ensuring cloud security best practices.

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