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Ml Devops Engineer Jobs (NOW HIRING)

Learn Beyond Consulting LLC

AI Devops Engineer

Atlanta, GA · On-site

$50.75 - $69.50/hr

AI/ML Operations & Observability: Hands-on experience supporting production AI/ML environments with ... DevOps Engineer IV to support the design, implementation, and optimization of enterprise AI ...

New

Anveta Inc

Senior DevOps Engineer (AI Platform)

Fort Mill, SC · On-site

$114K - $146K/yr

Senior DevOps Engineer (AI Platform) Location: Fort Mill, SC (Hybrid - 3 Days Onsite) Job Type ... The ideal candidate will also have exposure to AI/ML infrastructure and experience supporting ...

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 ...

Synergy ECP

Senior DevOps Engineer

Columbia, MD · On-site

$126K - $162K/yr

They are seeking a highly experienced Senior DevOps Engineer to focus on deploying infrastructure ... ML engineers and data scientists to streamline deployment processes and optimize resource ...

New York Life

$90K - $128K/yr

Experience supporting AI/ML operational workloads, including deployment of machine learning models and familiarity with Generative AI, LLMs, and AI-assisted DevOps tools such as Harness AI.

ACI Infotech

senior DevOps Engineer

Vienna, VA · On-site

$130K - $167K/yr

Senior DevOps Engineer Duration: 12+ Months Location: VA Senior DevOps Engineer that would ... ML, federated queries, ML, security - Expose data sources - Incorporate federation - expose data ...

4pconsultinginc

DevOps Engineer 4

Atlanta, GA · On-site

$50.75 - $69.50/hr

... DevOps Engineer (Level 4) to join our team. This role focuses on designing, deploying, and ... Build and maintain CI/CD pipelines to support ML models, data workflows, and web applications.

VuMedi Inc.

DevOps Engineer

Oakland, CA

$60.50 - $83/hr

You'll also support and enable emerging AI/ML and LLM-powered systems used for large-scale medical ... You have 5+ years of experience in DevOps, SRE, or infrastructure engineering, with a strong focus ...

WorkNovas LLC

DevOps Engineer

Reston, VA · On-site

$55 - $75.25/hr

... AI/ML-driven tools and automation to improve pipeline efficiency, anomaly detection, incident ... DevOps, Cloud Engineering, or related roles • Hands-on experience with CI/CD tools (GitLab ...

Raft Company Website

$129K - $166K/yr

S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in ... As a Senior DevOps Engineer, you will own the secure delivery patterns behind [R]DP, [R]AP, [R]AIMS ...

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 ...

Apex Systems

DevOps Engineer

Bentonville, AR · On-site

$45 - $61.50/hr

Experience supporting ML/AI workloads, including model serving infrastructure and GPU provisioning. * Google Cloud Platform Professional DevOps Engineer or Cloud Architect certification.

Fantom Corporation

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 ...

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 ...

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All JobsMl Devops Engineer Jobs

ML Devops Engineer information

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$19

$59

$90

How much do ml devops engineer jobs pay per hour?

As of Jul 23, 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.

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.

Is DevOps dead due to AI?

DevOps engineers focus on automating and streamlining software development and deployment processes. While AI tools are increasingly used to enhance automation and monitoring, they complement rather than replace the core DevOps practices, making the role still relevant and evolving with new technologies.

What engineer makes $500,000 a year?

A senior or lead Machine Learning DevOps Engineer with extensive experience, advanced skills in cloud platforms, automation, and infrastructure management can earn $500,000 or more annually, especially in high-cost-of-living areas or large tech companies. Such roles often require strong expertise in tools like Kubernetes, Docker, and CI/CD pipelines, along with relevant certifications and a track record of managing complex ML deployment 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.

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.

Is DevOps still in demand in 2026?

DevOps engineers remain in high demand in 2026 due to the ongoing need for automation, continuous integration, and deployment in software development. Skills in cloud platforms, containerization, and automation tools like Jenkins and Kubernetes are especially valuable in this field.

What is the salary of DevOps engineer vs ML engineer?

DevOps engineers typically earn between $80,000 and $140,000 annually, depending on experience and location, while ML engineers often have salaries ranging from $100,000 to $160,000 or higher. ML engineers usually require specialized skills in machine learning frameworks and data handling, which can influence compensation levels.

What are ML DevOps Engineers?

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.
More about ML Devops Engineer jobs
What job categories do people searching Ml Devops Engineer jobs look for? The top searched job categories for Ml Devops Engineer jobs are:
ML Devops Engineer jobs near you
Infographic showing various Ml Devops Engineer job openings in the United States as of July 2026, with employment types broken down into 90% Full Time, 2% Part Time, and 8% Contract. Highlights an 75% Physical, 8% Hybrid, and 17% Remote job distribution, with an average salary of $122,950 per year, or $59.1 per hour.

AI Devops Engineer

Learn Beyond Consulting LLC

Atlanta, GA • On-site

$50.75 - $69.50/hr

Other

Posted 2 days ago

Job description

Title : AI Devops Engineer

Location: Atlanta, GA

5 Month Contract

Only W2 

3 days hybrid onsite in Atlanta

Top Skills'' Details

Cloud Architecture & Automation: 10+ years of experience designing and deploying cloud infrastructure using Google Cloud Platform (preferred), Azure, or Databricks with strong Terraform/Bicep expertise.

DevSecOps & Governance: Proven ability to implement secure CI/CD pipelines, cloud security controls (IAM, encryption, secrets management), and governance frameworks.

AI/ML Operations & Observability: Hands-on experience supporting production AI/ML environments with model monitoring, drift detection, logging, alerting, and observability solutions

Job Description

We are seeking a Senior DevOps Engineer IV to support the design, implementation, and optimization of enterprise AI platforms and cloud infrastructure. This role will focus on cloud architecture, Infrastructure as Code (IaC), AI/ML operations, observability, security, governance, and Agentic AI systems. The ideal candidate will have extensive experience building scalable cloud environments, implementing DevOps best practices, and enabling production-grade AI solutions in regulated enterprise environments.

Key Responsibilities

Design, deploy, and maintain enterprise cloud infrastructure supporting AI/ML workloads.

Implement Infrastructure as Code using Terraform, Bicep, or similar automation tools.

Develop and manage CI/CD pipelines with integrated security, governance, and compliance controls.

Architect scalable, highly available AI platforms across cloud environments.

Drive FinOps practices, including cloud cost optimization, resource utilization, and enterprise cost visibility.

Implement AI observability frameworks covering model performance, drift detection, reliability, business KPIs, and operational monitoring.

Design and support AI/ML lifecycle management including monitoring, retraining strategies, logging, alerting, and incident response.

Embed security, compliance, and model risk management controls into AI development and deployment processes.

Support development and operationalization of Agentic AI solutions, including orchestration, monitoring, testing, and governance.

Establish best practices for AgentOps, model governance, and AI platform reliability

Additional Skills & Qualifications

Design and implementation of multi-cloud AI infrastructure with integrated governance and policy controls.

Experience embedding security, compliance, and governance controls directly into IaC and deployment pipelines.

Strong understanding of AI FinOps, including token optimization, cost-performance tradeoffs, and enterprise cost visibility.

Experience implementing model risk management controls, including auditability, explainability, and access governance.

Knowledge of designing AI systems for regulated environments and enforcing runtime guardrails and policy controls.

Ability to develop enterprise-wide AI observability strategies, covering model performance, data drift, bias detection, reliability, and business KPIs.

Experience implementing centralized monitoring frameworks and automated response mechanisms across AI platforms.

Exposure to LLM-based applications, AI agents, prompt engineering, API integrations, and orchestration frameworks such as LangChain.

Experience designing and supporting agent-based systems at scale, including multi-agent coordination, tool orchestration, memory management, and state management.

Knowledge of AgentOps practices, including AI deployment, testing, monitoring, iteration, and governance.

Understanding of autonomous AI system failure modes and mitigation strategies.

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