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Senior Data Engineer, AI

Reads like a ML Engineer role: putting models into production.

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
  • Architect and build AI workflows combining models, prompts, data, and business logic

  • Build production AI deployment pipelines and orchestration layers

  • Embed AI functionality into enterprise systems, APIs, and user interfaces

  • Apply guardrails for secure, transparent, and compliant AI behavior

  • Set up monitoring and observability for AI usage, performance, drift, and failures

From the employer’s posting
Architect and build AI workflows that merge models, prompts, enterprise data, tools, and business logic
Construct and sustain production-grade AI deployment pipelines
Embed AI functionality within enterprise systems, APIs, and user interfaces
Apply guardrails that guarantee secure, transparent, and compliant AI behavior
Deploy monitoring and observability for AI systems covering usage, performance, drift, and failures

What you’ll bring

All qualifications

Core experience

  • More than 3 years of software development experience
  • Mastery of prompt engineering: versioning, testing, and optimization
  • Experience building production AI pipelines, MLOps, and productionization
  • Monitoring and observability for AI systems (drift, performance, failures)
  • English at B2 level or above
Qualification wording
More than 3 years of experience working in software development
Mastery of prompt engineering approaches, covering versioning, testing, and optimization
Abilities in constructing production-grade AI deployment pipelines, MLOps, and productionization
Proficiency in monitoring and observability for AI systems, covering drift, performance, and failures
English skills at B2 level or above

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

Your next step

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Has EPAM Systems posted this job before?

Yes. We’ve followed 7 other Senior Data Engineer, AI jobs there since November 2026. All 7 are still up.

  1. Place not statedStill up after 3 days, first seen Nov 1
  2. Place not statedStill up after 3 days, first seen Nov 1
  3. Place not statedStill up after 3 days, first seen Nov 1
  4. Place not statedStill up after 3 days, first seen Nov 1
  5. Place not statedStill up after 3 days, first seen Nov 1
  6. Place not statedStill up after 3 days, first seen Nov 1
  7. Place not statedStill up after 3 days, first seen Nov 1
  8. This jobUp 3 days so far, first seen Nov 1

Large employers often post one title for several teams, so these can be separate openings.

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 are looking for a Senior Data Engineer with AI to architect and build intelligent AI workflows that merge models, enterprise data, and business logic into dependable, production-ready solutions. Within this position, you will construct scalable AI deployment pipelines, embed AI functionality into enterprise systems, and partner with data scientists and business experts to deliver secure, transparent, and compliant AI behavior. Responsibilities Architect and build AI workflows that merge models, prompts, enterprise data, tools, and business logic Create and sustain prompt engineering approaches, covering versioning, testing, and optimization Deploy orchestration layers supporting multi-step reasoning, decisioning, and action execution Embed AI functionality within enterprise systems, APIs, and user interfaces Apply guardrails that guarantee secure, transparent, and compliant AI behavior Construct and sustain production-grade AI deployment pipelines Guarantee dependability, scalability, latency optimization, and cost efficiency of AI services Deploy monitoring and observability for AI systems covering usage, performance, drift, and failures Set up change control, versioning, rollback, and release management practices Work closely alongside data scientists and business experts to verify model behavior and outputs Convert experimentation outcomes into dependable production-ready solutions Convey operational constraints and engineering considerations to stakeholders Requirements More than 3 years of experience working in software development Solid engineering foundation paired with applied AI expertise Proficiency in architecting and building AI workflows that merge models, prompts, and enterprise data Mastery of prompt engineering approaches, covering versioning, testing, and optimization Abilities in constructing production-grade AI deployment pipelines, MLOps, and productionization Proficiency in monitoring and observability for AI systems, covering drift, performance, and failures Understanding of change control, versioning, and release management practices Ability to rapidly get up to speed on new platforms and tools Experience with large-scale enterprise data ecosystems English skills at B2 level or above
Source details

View original posting ↗

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