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Experion Technologies (I) Pvt Ltd

407, 4th Floor, Thejaswini, Technopark Campus, Thiruvananthapuram, Kerala, India , 695581

Senior Machine Learning Engineer AWS ML Pipelines

Closing Date:20,May 2025
Job Published: 22,Apr 2025

Brief Description

Role : Senior Machine Learning Engineer  AWS ML Pipelines

Notice period - Immediate joiners

Location - Remote

 

Job Overview

 

We are seeking a highly skilled and independent Senior Machine Learning Engineer Contractor

to design, develop, and deploy advanced ML pipelines in an AWS environment. In this role, you

will build cutting-edge solutions that automate entity matching for master data management,

implement fraud detection systems, handle transaction matching, and integrate GenAI

capabilities. The ideal candidate will have extensive hands-on experience in AWS services such

as SageMaker, Bedrock, Lambda, Step Functions, and S3, as well as strong expertise in CI/CD

practices to ensure a robust and scalable solution.

 

Key Responsibilities

  • ML Pipeline Design & Development:
  • Architect, develop, and maintain end-to-end ML pipelines focused on entity

matching, fraud detection, and transaction matching.

  • Integrate generative AI (GenAI) solutions using AWS Bedrock to enhance data

processing and decision-making.

  • Collaborate with cross-functional teams to refine business requirements and

develop data-driven solutions tailored to master data management needs.

  • AWS Ecosystem Expertise:
  • Utilize AWS SageMaker for model training, deployment, and continuous

improvement.

  • Leverage AWS Lambda and Step Functions to orchestrate serverless workflows

for data ingestion, preprocessing, and real-time processing.

  • Manage data storage, retrieval, and scalability concerns using AWS S3.
  • CI/CD Implementation:
  • Develop and integrate automated CI/CD pipelines (using tools such as GitLab) to

streamline model testing, deployment, and version control.

  • Ensure rapid iteration and robust deployment practices to maintain high

availability and performance of ML solutions.

  • Data Security & Compliance:
  • Implement security best practices to safeguard sensitive data, ensuring

compliance with organizational and regulatory requirements.

  • Incorporate monitoring and alerting mechanisms to maintain the integrity and

performance of deployed ML models.

  • Collaboration & Documentation:
  • Work closely with business stakeholders, data engineers, and data scientists to

ensure solutions align with evolving business needs.

  • Document all technical designs, workflows, and deployment processes to support

ongoing maintenance and future enhancements.

  • Provide regular progress updates and adapt to changing priorities or business

requirements in a dynamic environment.

Required Qualifications

  • Technical Expertise:
  • 5+ years of professional experience in developing and deploying ML models and

pipelines.

  • Proven expertise in AWS services including SageMaker, Bedrock, Lambda, Step

Functions, and S3.

  • Strong proficiency in Python and/or PySpark for data manipulation, model

development, and pipeline implementation.

  • Demonstrated experience with CI/CD tools and methodologies, preferably with

GitLab or similar version control systems.

  • Practical experience in building solutions for entity matching, fraud detection, and

transaction matching within a master data management context.

  • Familiarity with generative AI models and their application within data

processing workflows.

  • Analytical & Problem-Solving Skills:
  • Ability to transform complex business requirements into scalable technical

solutions.

  • Strong data analysis capabilities with a track record of developing models that

provide actionable insights.

  • Communication & Collaboration:
  • Excellent verbal and written communication skills.
  • Demonstrated ability to work independently as a contractor while effectively

collaborating with remote teams.

  • Proven record of quickly adapting to new technologies and agile work

environments.

 

Preferred Qualifications

  • Bachelors or Master’s degree in Computer Science, Data Science, Engineering, or a

related field.

  • Experience with additional AWS services such as Kinesis, Firehose, and SQS.
  • Prior experience in a consulting or contracting role, demonstrating the ability to manage

deliverables under tight deadlines.

  • Experience within industries where data security and compliance are critical.

Preferred Skills

Key Responsibilities

  • ML Pipeline Design & Development:
  • Architect, develop, and maintain end-to-end ML pipelines focused on entity

matching, fraud detection, and transaction matching.

  • Integrate generative AI (GenAI) solutions using AWS Bedrock to enhance data

processing and decision-making.

  • Collaborate with cross-functional teams to refine business requirements and

develop data-driven solutions tailored to master data management needs.

  • AWS Ecosystem Expertise:
  • Utilize AWS SageMaker for model training, deployment, and continuous

improvement.

  • Leverage AWS Lambda and Step Functions to orchestrate serverless workflows

for data ingestion, preprocessing, and real-time processing.

  • Manage data storage, retrieval, and scalability concerns using AWS S3.
  • CI/CD Implementation:
  • Develop and integrate automated CI/CD pipelines (using tools such as GitLab) to

streamline model testing, deployment, and version control.

  • Ensure rapid iteration and robust deployment practices to maintain high

availability and performance of ML solutions.

  • Data Security & Compliance:
  • Implement security best practices to safeguard sensitive data, ensuring

compliance with organizational and regulatory requirements.

  • Incorporate monitoring and alerting mechanisms to maintain the integrity and

performance of deployed ML models.

  • Collaboration & Documentation:
  • Work closely with business stakeholders, data engineers, and data scientists to

ensure solutions align with evolving business needs.

  • Document all technical designs, workflows, and deployment processes to support

ongoing maintenance and future enhancements.

  • Provide regular progress updates and adapt to changing priorities or business needs