AWS DevOps / MLOps Engineer

Cydez Technologies Private Limited

3 - 7 years

4 - 7 Lacs

cochin kerala india

Posted:3 hours ago| Platform: Foundit logo

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Skills Required

3+ years of experience in devops or mlops. strong knowledge of aws services: sagemaker eks/ecs step functions. experience with ci/cd tools like jenkins github actions gitlab ci/cd or aws codepipeline. hands-on experience with infrastructure as code (terraform aws cdk). good scripting skills in python or go. familiarity with docker and container orchestration. understanding of ml model lifecycle: data ingestion training deployment monitoring retraining. experience with monitoring and logging tools (cloudwatch datadog). knowledge of gitops practices to manage infrastructure and ml models.

Work Mode

On-site

Job Type

Full Time

Job Description

About the Role:

We are seeking an AWS DevOps / MLOps Engineer to support our Agentic AI projects. The role involves building, automating, and managing cloud infrastructure and machine learning workflows on AWS, ensuring smooth deployment and operation of AI solutions.

Key Responsibilities:

Design, deploy, and manage AWS infrastructure (VPC, EC2, ECS/EKS, S3, IAM, Lambda, Step Functions).
Build and maintain CI/CD pipelines for deploying applications, data pipelines, and ML models.
Apply MLOps practices to automate model versioning, training, testing, deployment, and monitoring.
Use AWS SageMaker to manage model training, deployment, monitoring, and drift detection.
Automate data and ML workflows using Airflow, Step Functions, or Kubeflow.
Set up monitoring and alerting using tools like CloudWatch, Prometheus, or Grafana.

Required Skills & Qualifications:

3+ years of experience in DevOps, Cloud Engineering, or MLOps.
Strong knowledge of AWS services: SageMaker, S3, EKS/ECS, Lambda, API Gateway, Redshift, DynamoDB, Step Functions.
Experience with CI/CD tools like Jenkins, GitHub Actions, GitLab CI/CD, or AWS CodePipeline.
Hands-on experience with Infrastructure as Code (Terraform, CloudFormation, AWS CDK).
Good scripting skills in Python, Bash, or Go.
Familiarity with Docker, Kubernetes, and container orchestration.
Understanding of ML model lifecycle: data ingestion, training, deployment, monitoring, retraining.
Experience with monitoring and logging tools (CloudWatch, ELK, Prometheus, Datadog).
Knowledge of GitOps practices to manage infrastructure and ML models.

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