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Senior AI/ML Engineer

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 applications using large language models and generative AI

  • Implement prompt engineering strategies and RAG architectures

  • Build frameworks to evaluate model performance, safety, and alignment

  • Create interfaces between generative AI models and production systems

  • Develop responsible AI mechanisms and guardrails for deployment

From the employer’s posting
Design applications utilizing large language models (LLMs) and generative AI technologies
Implement prompt engineering strategies and Retrieval-Augmented Generation (RAG) architectures
Build frameworks to evaluate model performance, safety, and alignment
Create interfaces between generative AI models and production systems
Develop mechanisms for responsible AI and guardrails for deployment

What you’ll bring

All qualifications

Core experience

  • Background developing applications with generative AI models
  • Expertise in prompt engineering and RAG architecture
  • Proficiency in Python
  • Knowledge of AI/ML frameworks such as LangChain
  • Familiarity with vector databases and embedding models
Qualification wording
Background in developing applications with generative AI models
Expertise in prompt engineering and RAG architecture
Proficiency in Python
Knowledge of modern AI/ML frameworks (e.g., LangChain)
Familiarity with vector databases and embedding models

Tools in this posting

  • Python
  • Azure
Source — Tool mentions in context
We are seeking a Senior AI/ML Engineer who will be responsible for designing, implementing, and optimizing generative AI solutions for applications. Responsibilities Design applications utilizing large language models (LLMs) and generative AI technologies Implement prompt engineering strategies and Retrieval-Augmented Generation (RAG) architectures Build frameworks to evaluate model performance, safety, and alignment Create interfaces between generative AI models and production systems Develop mechanisms for responsible AI and guardrails for deployment Stay updated on advancements in generative AI research and best practices Requirements Background in developing applications with generative AI models Expertise in prompt engineering and RAG architecture Proficiency in Python, with added value for experience in asynchronous applications Knowledge of modern AI/ML frameworks (e.g., LangChain) Familiarity with vector databases and embedding models Understanding of responsible AI practices and mitigation strategies for hallucinations Competency in working with cloud-based AI services (e.g., OpenAI API, Azure AI) Nice to have Background in developing solutions for specific industries such as life sciences, finance, or insurance Familiarity with LLM tracing, evaluation datasets, and experiment management Understanding of techniques for evaluating RAG systems or other LLM architectures Skills in automated prompt optimization tools (e.g., dspy) Expertise in creating rapid prototypes

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 4,284 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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Has EPAM Systems posted this job before?

Yes. We’ve followed one other Senior AI/ML Engineer job there since November 2026. It is still up.

  1. Place not statedStill up after 3 days, first seen Nov 1
  2. This jobUp 3 days so far, first seen Nov 1

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 seeking a Senior AI/ML Engineer who will be responsible for designing, implementing, and optimizing generative AI solutions for applications. Responsibilities Design applications utilizing large language models (LLMs) and generative AI technologies Implement prompt engineering strategies and Retrieval-Augmented Generation (RAG) architectures Build frameworks to evaluate model performance, safety, and alignment Create interfaces between generative AI models and production systems Develop mechanisms for responsible AI and guardrails for deployment Stay updated on advancements in generative AI research and best practices Requirements Background in developing applications with generative AI models Expertise in prompt engineering and RAG architecture Proficiency in Python, with added value for experience in asynchronous applications Knowledge of modern AI/ML frameworks (e.g., LangChain) Familiarity with vector databases and embedding models Understanding of responsible AI practices and mitigation strategies for hallucinations Competency in working with cloud-based AI services (e.g., OpenAI API, Azure AI) Nice to have Background in developing solutions for specific industries such as life sciences, finance, or insurance Familiarity with LLM tracing, evaluation datasets, and experiment management Understanding of techniques for evaluating RAG systems or other LLM architectures Skills in automated prompt optimization tools (e.g., dspy) Expertise in creating rapid prototypes
Source details

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