Senior/lead ML Engineering Role

Summary

We are seeking an MLOps Engineer with 3–6 years of experience. This role will be responsible for implementing MLOps solutions on AWS, building and maintaining machine learning pipelines, and supporting model deployment, monitoring, and inference workflows.

The ideal candidate has hands-on experience with Amazon SageMaker, GitLab CI/CD, Docker, AWS CDK, and the machine learning lifecycle.

Key Responsibilities

• Implement MLOps solutions using Amazon SageMaker, AWS Lambda, Docker, YAML, and API Gateway.

• Build and maintain SageMaker Pipelines.

• Build and maintain GitLab CI/CD pipelines.

• Create solutions for Docker image management and security.

• Perform artifact scanning and vulnerability checks.

• Create and manage APIs.

• Manage endpoints and inference pipelines.

• Enable model monitoring and data drift.

• Develop solutions using AWS CDK (Python).

Required Qualifications

• 3–6 years of experience.

• Understanding of MLOps concepts, including:

• • Model Training

• • Feature Engineering

• • Model Registration

• • Endpoint Deployment

• • Batch Inference

• • Model Monitoring

• • Data Drift

• Experience implementing MLOps using:

• • Amazon SageMaker

• • AWS Lambda

• • Docker

• • YAML

• • API Gateway

• Experience with GitLab and GitLab CI/CD Pipelines

• Experience using AWS CDK (Python)