AWS Machine Learning Engineer /Mlops Engineer

Role Overview

We are looking for an AWS Machine Learning Engineer to join India Data Science team of DolFinTech (https://www.dolfintech.com/). The candidate must have at least 2 years of direct hands-on experience in developing, deploying, integrating, and monitoring Machine Learning models and processes on AWS. The candidate must have hands-on experience with

Amazon SageMaker, AWS Lambda, API Gateway, S3, CloudWatch, and AWS-based ETL/data pipelines.

Key Responsibilities

• Develop, package, and deploy ML models using Amazon SageMaker.
• Build and manage real-time SageMaker inference endpoints.
• Develop AWS Lambda functions for model invocation and application integration.
• Create and maintain REST/HTTP APIs using Amazon API Gateway.
• Build and maintain ETL/data-processing pipelines on AWS using services such as S3, Glue, Lambda, Athena, and/or Step Functions.
• Implement end-to-end ML scoring workflows such as:
• Application/API → API Gateway → Lambda → SageMaker → Response
• Implement logging, monitoring, and alerting using Amazon CloudWatch.
• Monitor model/API performance, latency, failures, and production issues.
• Troubleshoot AWS deployment, integration, and IAM/permission issues.
• Support model and code versioning and deployment across Development, UAT/Staging, and Production environments.
• Work with Data Scientists to convert notebook/prototype models into reliable production solutions.

Mandatory Skills

• Minimum 2 years of hands-on AWS experience
• Strong hands-on experience with Amazon SageMaker, AWS Lambda , Amazon API Gateway, Amazon S3, Amazon CloudWatch, AWS IAM, AWS ETL/data-processing pipelines
• Strong Python and SQL skills.
• Experience deploying ML models into production environments.
• Experience creating and consuming REST APIs / JSON interfaces.
• Experience with Git/version control.
• Good understanding of ML models, feature engineering, model scoring, and model monitoring.

Preferred Skills

Experience with some of the following would be advantageous

• AWS Glue / Athena
• AWS Step Functions / Event Bridge
• SageMaker Pipelines / Model Registry / Model Monitor
• Docker / Amazon ECR
• CI/CD pipelines
• Terraform / CloudFormation / AWS CDK
• Fraud, credit risk, transaction risk, or financial-services models

Experience & Qualification

• 3+ years overall experience preferred
• Minimum 2 years of direct hands-on AWS experience
• Bachelor's/Master's degree in Computer Science, Engineering, Data Science, or a related discipline