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Lead AI Solution Engineer with Databricks, ML

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 and optimize feature engineering pipelines for batch and real-time workloads

  • Build, deploy, and maintain model training and inference pipelines

  • Develop streaming solutions using Spark Streaming

  • Enable real-time feature generation and model serving

  • Operate and maintain production ML systems for consistent performance

From the employer’s posting
Design and optimize feature engineering pipelines for both batch and real-time workloads
Build, deploy, and maintain scalable model training and inference pipelines
Develop streaming solutions using Spark Streaming
Enable real-time feature generation and model serving
Operate and maintain production ML systems to ensure consistent performance

What you’ll bring

All qualifications

Core experience

  • 7+ years building and operating offline and online feature pipelines
  • Proficiency in Databricks, Spark/PySpark, Python, and SQL
  • Expertise in model training and real-time inferencing pipelines
  • Experience with real-time feature generation and low-latency model inference
  • Proven track record of running production-grade ML systems
Qualification wording
7+ years of experience building, deploying, and operating offline and online feature engineering pipelines
Proficiency in Databricks, Spark/PySpark, Python, and SQL
Expertise in model training and real-time inferencing pipelines
Experience with real-time feature generation and low-latency model inference
Proven track record of running production-grade ML systems

Tools in this posting

  • Python
  • SQL
  • AWS
  • Databricks
  • MLflow
  • Spark
  • PySpark
Source — Tool mentions in context
We are looking for a Lead AI Solution Engineer who brings strong expertise in Databricks and Machine Learning to architect, launch, and manage production-level ML systems. The ideal candidate works hands-on as a Data Engineer or Machine Learning Engineer, has demonstrated success delivering both real-time and batch ML solutions, possesses solid skills in building streaming pipelines, and brings experience maintaining the scalability and reliability of ML systems once they are live in production. Responsibilities Design and optimize feature engineering pipelines for both batch and real-time workloads Build, deploy, and maintain scalable model training and inference pipelines Process structured and unstructured data at scale Develop streaming solutions using Spark Streaming Enable real-time feature generation and model serving Ensure reliability, scalability, and performance of ML solutions in production Support batch pipelines for model training and retraining Operate and maintain production ML systems to ensure consistent performance Requirements 7+ years of experience building, deploying, and operating offline and online feature engineering pipelines Proficiency in Databricks, Spark/PySpark, Python, and SQL Expertise in model training and real-time inferencing pipelines Experience processing large-scale unstructured data Knowledge of Spark Streaming Experience with real-time feature generation and low-latency model inference Proven track record of running production-grade ML systems English proficiency at B2 level or higher Nice to have Exposure to MLflow and established MLOps workflows Hands-on background with AWS Prior work in Customer Analytics, Recommendation Systems, or Retail industries

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
3
Last seen by us
Nov 3, 2026
Employer says posted
Oct 30, 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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Has EPAM Systems posted this job before?

Yes. We’ve followed one other Lead AI Solution Engineer with Databricks, ML 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 looking for a Lead AI Solution Engineer who brings strong expertise in Databricks and Machine Learning to architect, launch, and manage production-level ML systems. The ideal candidate works hands-on as a Data Engineer or Machine Learning Engineer, has demonstrated success delivering both real-time and batch ML solutions, possesses solid skills in building streaming pipelines, and brings experience maintaining the scalability and reliability of ML systems once they are live in production. Responsibilities Design and optimize feature engineering pipelines for both batch and real-time workloads Build, deploy, and maintain scalable model training and inference pipelines Process structured and unstructured data at scale Develop streaming solutions using Spark Streaming Enable real-time feature generation and model serving Ensure reliability, scalability, and performance of ML solutions in production Support batch pipelines for model training and retraining Operate and maintain production ML systems to ensure consistent performance Requirements 7+ years of experience building, deploying, and operating offline and online feature engineering pipelines Proficiency in Databricks, Spark/PySpark, Python, and SQL Expertise in model training and real-time inferencing pipelines Experience processing large-scale unstructured data Knowledge of Spark Streaming Experience with real-time feature generation and low-latency model inference Proven track record of running production-grade ML systems English proficiency at B2 level or higher Nice to have Exposure to MLflow and established MLOps workflows Hands-on background with AWS Prior work in Customer Analytics, Recommendation Systems, or Retail industries
Source details

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