Role Overview

We are looking for an Data Science
AWS Engineer to join India Data Science team of DolFinTech, USA.
The candidate must have at
least 1 year 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 1 year of hands-on AWS and Machine Learning experience
Solid hands-on experience with Amazon SageMaker, AWS Lambda , Amazon API Gateway, Amazon S3, Amazon CloudWatch, AWS IAM, AWS ETL/data-processing pipelines, AWS Glue, Athena
Solid
Python
and
SQL
skills.
Experience deploying ML models into
production environments
.
Experience creating and consuming
REST APIs / JSON interfaces
.
Experience with
Git/version control
.
Valuable understanding of ML models, feature engineering, model scoring, and model monitoring.

Preferred Skills

Experience with some of the

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