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Ml Devops Engineer Jobs (NOW HIRING)

Esolvit, Inc.

Senior DevOps Engineer

Austin, TX · On-site

$128K - $165K/yr

Senior DevOps Engineer Location: Austin, TX Duration: Long Term Senior DevOps Engineer with ... ML concepts and platform integration * 8 Years of Experience with Azure Monitoring and Log ...

DeepSig, Inc

DevOps Engineer

Arlington, VA · On-site

$60.75 - $83.25/hr

We are seeking an experienced DevOps Engineer to work with engineering and QA teams to automate and optimize software and ML model development and validation, supporting existing and upcoming product ...

DeepSig Inc Careers Page

DevOps Engineer

Arlington, VA · On-site

$60.75 - $83.25/hr

We are seeking an experienced DevOps Engineer to work with engineering and QA teams to automate and optimize software and ML model development and validation, supporting existing and upcoming product ...

DeepSig Inc

DevOps Engineer

Arlington, VA · Hybrid

$60.75 - $83.25/hr

We are seeking an experienced DevOps Engineer to work with engineering and QA teams to automate and optimize software and ML model development and validation, supporting existing and upcoming product ...

Engg

DevOps Engineer

Palo Alto, CA · On-site

$62.25 - $85.25/hr

We're looking for a DevOps Engineer to build and operate the infrastructure that enables engineers ... You'll work across software, ML, and robotics teams to automate infrastructure, streamline ...

New

CACI

Expert DevOps Engineer

Fort Liberty, NC · On-site

$51.50 - $70.50/hr

Expert DevOps Engineer Job Category: Information Technology Time Type: Full time Minimum Clearance ... As part of a team, you will deploy AI/ML capabilities using multiple technological and ...

New

Cliff Services Inc

Devops Engineer

Richardson, TX · On-site

$48 - $65.50/hr

Python Scripting * Kubernetes * DevOps (CI/CD) * Terraform (Infrastructure as Code) * Cloud ... AI/ML Platform exposure Experience: 6+ Years (Preferred)

Fantom Corporation

DevOps Engineer

Chantilly, VA

$54 - $74/hr

We are seeking a DevOps Engineer to design, implement, and maintain secure DevOps infrastructure ... Learning (ML) with a focus on identifying trends, object detection, and classification of ...

Venus Consultancy

DevOps Engineer

Cambridge, MA · On-site

$57.75 - $79/hr

DevOps Engineer U.S. GenAI startup, Cambridge Office Full-Time Employment with We . We are ... Knowledge of AI/ML infrastructure requirements and optimization * Experience with GPU orchestration ...

Venus Consultancy

DevOps Engineer

Cambridge, MA · On-site

$57.75 - $79/hr

DevOps Engineer U.S. GenAI startup, Cambridge Office Full-Time Employment with We . We are ... Knowledge of AI/ML infrastructure requirements and optimization * Experience with GPU orchestration ...

Fantom

DevOps Engineer

Chantilly, VA · On-site

$54 - $74/hr

We are seeking a DevOps Engineer to design, implement, and maintain secure DevOps infrastructure ... Learning (ML) with a focus on identifying trends, object detection, and classification of ...

FICO

Lead ML Ops/DevOps Engineer - AI Engineering

$140K - $220K/yr

Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructure. * Work closely with Data Scientists and ML Engineers to ...

FICO

Lead ML Ops/DevOps Engineer - AI Engineering

$140K - $220K/yr

Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructure. * Work closely with Data Scientists and ML Engineers to ...

Arcarithm, Inc.

OpenShift DevOps Engineer

Tullahoma, TN · On-site

$45.75 - $62.50/hr

OpenShift DevOps Engineer Job Location: Tullahoma, TN Clearance Required: Active Secret Security ... ML solutions, and cloud‑native services. The ideal candidate will possess extensive experience ...

Opiniion

DevOps Engineer

Lehi, UT · On-site

$49.50 - $67.75/hr

We're looking for a Staff DevOps Engineer to own the reliability, scalability, and security of our ... Experience supporting AI/ML workloads or MLOps pipelines on AWS. * Background in high‑growth SaaS ...

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ML Devops Engineer information

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How much do ml devops engineer jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for ml devops engineer in the United States is $59.11, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $69.23 per hour, depending on experience, location, and employer.

What is an ML DevOps engineer?

ML DevOps Engineers are professionals who bridge the gap between machine learning (ML) development and operations (DevOps). They are responsible for automating, deploying, monitoring, and maintaining machine learning models in production environments. Their work ensures that ML models are scalable, reliable, and integrated seamlessly within an organization's infrastructure. ML DevOps Engineers collaborate with data scientists, software engineers, and IT teams to streamline the ML lifecycle from model development to deployment and monitoring.

How does an ML DevOps engineer typically collaborate with data scientists and software engineers on machine learning projects?

An ML DevOps Engineer plays a crucial role in bridging the gap between data scientists and software engineers by operationalizing machine learning models. They work closely with data scientists to understand model requirements and assist in preparing models for deployment, ensuring scalability and reliability. Additionally, they collaborate with software engineers to integrate models into production systems, automate workflows, and maintain infrastructure. This cross-functional teamwork often involves regular meetings, code reviews, and shared documentation, fostering a collaborative and agile environment.

What are the key skills and qualifications needed to thrive as an ML DevOps engineer, and why are they important?

To thrive as an ML DevOps Engineer, you need strong skills in machine learning, software engineering, and cloud infrastructure, often supported by a degree in computer science or related fields. Familiarity with tools like Docker, Kubernetes, CI/CD systems, and platforms such as AWS or Azure, as well as experience with MLOps frameworks, is typically required. Excellent problem-solving, collaboration, and communication skills help you bridge the gap between data science and engineering teams. These competencies are crucial for reliably deploying, scaling, and maintaining machine learning models in production environments.

What is the difference between Ml Devops Engineer vs Data Scientist?

AspectMl Devops EngineerData Scientist
Required SkillsMachine learning, DevOps tools, scripting, cloud platformsStatistics, data analysis, machine learning, programming
Work EnvironmentCollaborates with DevOps and ML teams, focuses on deployment and automationAnalyzes data, builds models, interprets results
CertificationsCloud certifications, ML certifications, DevOps toolsData science certifications, statistical courses

The main difference between an Ml Devops Engineer and a Data Scientist lies in their focus areas. Ml Devops Engineers specialize in deploying, automating, and maintaining machine learning models within production environments, combining DevOps practices with ML expertise. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require knowledge of machine learning, but their responsibilities and skill sets differ significantly.

Are ML Devops engineers still in demand?

ML DevOps engineers are in high demand due to the increasing adoption of machine learning models in production environments. Skills in cloud platforms, automation, and containerization tools like Docker and Kubernetes are highly valued in this role, which supports the deployment, monitoring, and scaling of ML systems.

Is ML DevOps Engineer still in demand in 2026?

ML DevOps Engineers are expected to remain in high demand in 2026 due to the growing adoption of machine learning and AI across industries. Skills in cloud platforms, automation, and containerization tools like Kubernetes and Docker will continue to be valuable for managing ML pipelines and deployments.
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ML Devops Engineer jobs near you
Infographic showing various Ml Devops Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 89% Full Time, 7% Part Time, and 3% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $122,950 per year, or $59.1 per hour.

Senior DevOps Engineer

Austin, TX • On-site

Esolvit, Inc.
IT Services • 51 - 200 employees
Learn more about Esolvit, Inc.external

$128K - $165K/yr

Full-time

Re-posted 2 days ago

Job description

Job title: Senior DevOps Engineer
Location: Austin, TX
Duration: Long Term
Job Description: Senior DevOps Engineer with expertise in AI integration, Azure cloud platform, and Snowflake administration, responsible for building scalable and secure data and AI pipelines.
Strong experience in CI/CD, automation using Python, and cloud infrastructure, ensuring efficient deployment, monitoring, and performance optimization.
Provides end-to-end DevOps support, including security, governance (RBAC), and collaboration with cross-functional teams to deliver reliable cloud and data solutions.
Required Skill:
  • 8 Years of Strong experience in DevOps practices and tools (Azure DevOps, Git, CI/CD pipelines)
  • 8 Years of Hands-on experience with Microsoft Azure cloud platform
  • 8 Years of Expertise in Snowflake administration including: Access control and security (RBAC, least privilege) ; Performance tuning and warehouse management
  • 8 Years of Proficiency in Python scripting for automation and data engineering tasks
  • 8 Years of Experience with containerization and orchestration (AKS preferred)
  • 8 Years of Knowledge of data pipelines, ETL processes, and cloud data integration
  • 8 Years of Understanding of AI/ML concepts and platform integration
  • 8 Years of Experience with Azure Monitoring and Log Analytics, including: Centralized logging, alerting, and dashboarding Security monitoring using audit logs and metrics
  • 8 Years of Familiarity with monitoring, logging, and observability tools
Preferred Skills:
  • 3 Years of Experience implementing AI-enabled data platforms (e.g., Snowflake Cortex / ML pipelines)
  • 3 Years of Experience with Infrastructure as Code
  • 3 Years of Knowledge of data security, compliance, and governance frameworks
  • 3 Years of Prior experience in public sector or large enterprise environments
  • 2 Years of Strong problem-solving and analytical skills
  • 2 Years of Ability to manage multiple priorities in a fast-paced environment
  • 2 Years of Attention to detail and commitment to best practices
  • 2 Years of Snowflake AI / Cortex features exposure
  • 1 Year of Excellent communication and documentation skills

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