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Senior ML Infrastructure Engineer (DevOps)
- 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 postingKeep real-time inference services healthy through alerting, live debugging, incident fixes, rollbacks and postmortems
Build and improve AWS infrastructure, CI/CD, Kubernetes, autoscaling, monitoring and cost control
Manage the GPU fleet, including driver and CUDA upgrades, node provisioning and capacity
Run real-time data pipelines feeding an online feature store and low-latency endpoints
Orchestrate batch jobs with Airflow or Databricks Workflows
From the employer’s posting
Keep real-time inference services healthy through alerting, live debugging and fixing of production incidents, rollbacks and postmortems
Build and improve AWS infrastructure, including infrastructure as code, CI/CD pipelines, Kubernetes, Docker, autoscaling, monitoring, load testing and cost control
Care for the GPU fleet through NVIDIA driver and CUDA upgrades, node provisioning and debugging, and capacity management
Run real-time production data pipelines, including streaming ingestion into an online feature store and serving features to low-latency endpoints
Orchestrate batch jobs with Airflow or Databricks Workflows
Posting record
Report an errorActively 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
- 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
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- AWS
- Databricks
- Docker
- Kubernetes
- Airflow
Where the posting names them
We are looking for a Senior ML Infrastructure Engineer who enjoys running systems in production. You will work side by side with our applied scientists: they build the models, and you own everything needed to run them reliably for millions of customers. Our team owns its infrastructure end to end, deployment and operations included, so your work is visible, and the ownership is real. Experience with 24x7 on-call rotations for high-load online services in production is required for this role. You will thrive here if you have been the person who gets paged and fixes things, rather than handing deployment and operations to a separate team. Responsibilities Keep real-time inference services healthy through alerting, live debugging and fixing of production incidents, rollbacks and postmortems, as part of a shared 24x7 on-call rotation Build and improve AWS infrastructure, including infrastructure as code, CI/CD pipelines, Kubernetes, Docker, autoscaling, monitoring, load testing and cost control Care for the GPU fleet through NVIDIA driver and CUDA upgrades, node provisioning and debugging, and capacity management Automate user access, including SSH credentials, service accounts and developer environments for the science team Run real-time production data pipelines, including streaming ingestion into an online feature store and serving features to low-latency endpoints Orchestrate batch jobs with Airflow or Databricks Workflows Partner with applied scientists to productionize their prototypes and keep them healthy Write and review production Python, including validating AI-generated code Requirements 24x7 on-call experience with high-load online services in production, including real incidents personally debugged and fixed live Strong Linux systems skills, with hands-on experience in Kubernetes, Docker and infrastructure as code Solid experience in Python, Git and CI/CD Experience with workflow orchestration tools such as Airflow, Databricks Workflows or equivalent Proficiency in Apache Airflow, CI/CD and DevOps practices Familiarity with Gen AI in SDLC English proficiency at B2 level or higher Nice to have Familiarity with Anthropic Claude Code
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
Already applied? Track this application
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
We are looking for a Senior ML Infrastructure Engineer who enjoys running systems in production. You will work side by side with our applied scientists: they build the models, and you own everything needed to run them reliably for millions of customers. Our team owns its infrastructure end to end, deployment and operations included, so your work is visible, and the ownership is real. Experience with 24x7 on-call rotations for high-load online services in production is required for this role. You will thrive here if you have been the person who gets paged and fixes things, rather than handing deployment and operations to a separate team. Responsibilities Keep real-time inference services healthy through alerting, live debugging and fixing of production incidents, rollbacks and postmortems, as part of a shared 24x7 on-call rotation Build and improve AWS infrastructure, including infrastructure as code, CI/CD pipelines, Kubernetes, Docker, autoscaling, monitoring, load testing and cost control Care for the GPU fleet through NVIDIA driver and CUDA upgrades, node provisioning and debugging, and capacity management Automate user access, including SSH credentials, service accounts and developer environments for the science team Run real-time production data pipelines, including streaming ingestion into an online feature store and serving features to low-latency endpoints Orchestrate batch jobs with Airflow or Databricks Workflows Partner with applied scientists to productionize their prototypes and keep them healthy Write and review production Python, including validating AI-generated code Requirements 24x7 on-call experience with high-load online services in production, including real incidents personally debugged and fixed live Strong Linux systems skills, with hands-on experience in Kubernetes, Docker and infrastructure as code Solid experience in Python, Git and CI/CD Experience with workflow orchestration tools such as Airflow, Databricks Workflows or equivalent Proficiency in Apache Airflow, CI/CD and DevOps practices Familiarity with Gen AI in SDLC English proficiency at B2 level or higher Nice to have Familiarity with Anthropic Claude Code
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