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

Work arrangement
No