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Data Software Engineer / MLOps / MLE

Pay
Pay not listed
Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting
Employment
Not stated

What you’ll work on

Full posting
  • Design scalable data pipelines using Python, Spark, and PySpark

  • Build AWS infrastructure and manage deployments through Databricks

  • Set up CI/CD workflows for machine learning models with Jenkins

  • Build, train, and release machine learning models with Amazon SageMaker

  • Write unit tests covering all phases of machine learning processes

From the employer’s posting
Design data pipelines that scale efficiently using Python, Spark, and PySpark
Construct AWS infrastructure and manage deployments through Databricks
Establish CI/CD workflows for machine learning models with Jenkins and similar platforms
Apply Amazon SageMaker to construct, train, and release machine learning models
Develop unit tests covering all phases of machine learning processes

What you’ll bring

All qualifications

Core experience

  • Strong knowledge of Python, Spark, and PySpark
  • Hands-on experience with AWS and Databricks
  • MLOps experience, including Jenkins
  • Experience with SageMaker for the ML lifecycle
  • Experience with CI/CD platforms and Terraform
Qualification wording
Knowledge of Python, Spark, and PySpark
Background in AWS and Databricks
Expertise in MLOps methodologies and tools including Jenkins
Skills in using Amazon SageMaker for machine learning lifecycle management
Experience with CI/CD platforms and Terraform for infrastructure automation

Tools in this posting

  • Python
  • AWS
  • Databricks
  • SageMaker
  • Spark
  • Terraform
  • Airflow
  • PySpark
Source — Tool mentions in context
We welcome a Data Software Engineer / MLOps / MLE to become part of our team and contribute to enabling multi-country operations for the LATAM region. A candidate with a strong foundation in Python, Spark, and PySpark, along with hands-on experience in AWS and Databricks, will thrive in this role. Responsibilities Design data pipelines that scale efficiently using Python, Spark, and PySpark Construct AWS infrastructure and manage deployments through Databricks Establish CI/CD workflows for machine learning models with Jenkins and similar platforms Apply Amazon SageMaker to construct, train, and release machine learning models Track model performance and drive improvements in efficiency Uphold standards for data quality and integrity at every stage Engage with teams across functions to generate innovative use cases Develop unit tests covering all phases of machine learning processes Modify data science pipelines to align with project demands Exchange information clearly to resolve questions and address technical matters Requirements Knowledge of Python, Spark, and PySpark Background in AWS and Databricks Expertise in MLOps methodologies and tools including Jenkins Skills in using Amazon SageMaker for machine learning lifecycle management Experience with CI/CD platforms and Terraform for infrastructure automation Flexibility to approach new use cases with ease Understanding of principles behind production-ready data science pipelines Background in preparing data and building models Competency in designing unit tests for each step of machine learning workflows Ability to communicate ideas and technical details effectively Nice to have Familiarity with Apache Airflow Knowledge of practices that ensure high data quality Certification in Databricks Certification in Jenkins

Posting record

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Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 1, 20262 days ago
Recorded sightings
4
Last seen by us
Nov 4, 2026
Employer says posted
Sep 29, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 2

Early. Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

EPAM Systems is hiring

656 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

We welcome a Data Software Engineer / MLOps / MLE to become part of our team and contribute to enabling multi-country operations for the LATAM region. A candidate with a strong foundation in Python, Spark, and PySpark, along with hands-on experience in AWS and Databricks, will thrive in this role. Responsibilities Design data pipelines that scale efficiently using Python, Spark, and PySpark Construct AWS infrastructure and manage deployments through Databricks Establish CI/CD workflows for machine learning models with Jenkins and similar platforms Apply Amazon SageMaker to construct, train, and release machine learning models Track model performance and drive improvements in efficiency Uphold standards for data quality and integrity at every stage Engage with teams across functions to generate innovative use cases Develop unit tests covering all phases of machine learning processes Modify data science pipelines to align with project demands Exchange information clearly to resolve questions and address technical matters Requirements Knowledge of Python, Spark, and PySpark Background in AWS and Databricks Expertise in MLOps methodologies and tools including Jenkins Skills in using Amazon SageMaker for machine learning lifecycle management Experience with CI/CD platforms and Terraform for infrastructure automation Flexibility to approach new use cases with ease Understanding of principles behind production-ready data science pipelines Background in preparing data and building models Competency in designing unit tests for each step of machine learning workflows Ability to communicate ideas and technical details effectively Nice to have Familiarity with Apache Airflow Knowledge of practices that ensure high data quality Certification in Databricks Certification in Jenkins
Source details

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