MLOps Engineer

2 weeks ago

Pune, India Ascentt Full time

Requirements

We are seeking a highly skilled and motivated MLOps Engineer to join our team. The ideal candidate will have extensive experience with AWS services, containerization, infrastructure as code, and continuous integration/continuous deployment (CI/CD) pipelines. You will be responsible for building and maintaining robust machine learning infrastructure, ensuring seamless deployment, scalability, and monitoring of machine learning models in production. A strong understanding of the machine learning lifecycle is essential to ensure efficient model development, deployment, and maintenance.


Key Responsibilities:

Design, implement, and maintain scalable and secure cloud infrastructure on AWS, including VPC, EC2, RDS, S3, Route53, SNS, CloudFront, CloudWatch, and IAM.

Deploy and manage containerized applications using ECS or EKS.

Develop and manage CI/CD pipelines using Jenkins and GitHub.

Build, test, and deploy Docker containers.

Implement and manage infrastructure as code using Terraform.

Develop automation scripts and tools using Python.

Design and implement API gateways and serverless functions using Lambda.

Utilize AWS SageMaker for machine learning model training, deployment, and monitoring, including pipelines and managed jobs.

Schedule and manage workflows using Apache Airflow.

Monitor and optimize the performance of machine learning models and infrastructure.

Collaborate with data scientists, software engineers, and other stakeholders to streamline the model deployment process.

Ensure security, reliability, and compliance of the ML infrastructure.

Apply knowledge of the machine learning lifecycle to support model development, deployment, monitoring, and refinement.

Work with data scientists to understand the requirements and constraints of different stages in the ML lifecycle.


Required Skills and Experience:

Strong experience with AWS services including VPC, EC2, RDS, S3, Route53, SNS, CloudFront, CloudWatch, IAM, ECS or EKS.

Proficiency in using Jenkins for CI/CD pipelines.

Expertise in GitHub for version control and collaboration.

Extensive experience with Docker for containerization.

Proficiency in Terraform for infrastructure as code.

Strong programming skills in Python.

Experience with API Gateway and Lambda functions.

Knowledge of AWS SageMaker, including pipelines and managed jobs.

Experience with Apache Airflow for workflow scheduling.

Excellent problem-solving skills and attention to detail.

Strong communication and collaboration skills.

Solid understanding of the machine learning lifecycle and best practices for deploying and maintaining ML models in production.


Preferred Qualifications:

AWS Certified Solutions Architect or similar certifications.

Experience with machine learning frameworks and libraries.

Familiarity with data engineering tools and practices.

Understanding of DevOps practices and principles.


  • Virtual Private Cloud ( VPC), EC2, RDS, S3, Route53, SNS, CloudFront, CloudWatch IAM
  • ECS or EKS
  • AWS
  • Jenkins
  • Github
  • Docker
  • Terraform - need good exp
  • Python - Need good exp
  • API Gateways
  • Lambda
  • R53
  • SageMaker - Pipelines, Managed jobs
  • Airflow

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