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Mlops Engineer Remote Jobs (NOW HIRING)

Home Depot

Senior AI Engineer (Remote)

Atlanta, GA · On-site +1

$99K - $136K/yr

The Senior AI Engineer is responsible for designing, building, scaling, and optimizing production ... Infrastructure & MLOps: Expertise in cloud-native architectures, containerization (Docker) and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

CSAA

Job Title Software Engineer III - AI/ML Platform Operations - Remote Requisition Number R7739 ... MLOps, Automation & Observability * Design and implement automation, monitoring, observability, and ...

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Mlops Engineer Remote information

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$115.9K

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How much do mlops engineer remote jobs pay per year?

As of Sep 1, 2026, the average yearly pay for mlops engineer remote in the United States is $115,864.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $151,500.00 per year, depending on experience, location, and employer.

What does an MLOps engineer do in a remote role?

An MLOps Engineer is responsible for streamlining and automating the deployment, monitoring, and management of machine learning models in production environments. Working remotely, they collaborate with data scientists, software engineers, and IT teams using cloud-based tools to ensure that ML models are scalable, reliable, and maintainable. Their tasks often include setting up CI/CD pipelines for ML workflows, managing model versioning, and monitoring model performance over time. Remote MLOps Engineers leverage communication and project management tools to stay aligned with distributed teams and ensure seamless operations.

What are common challenges faced by remote MLOps engineers, and how can they be addressed?

Remote MLOps Engineers often encounter challenges related to communication and collaboration, especially when coordinating with data scientists, developers, and operations teams across different time zones. To overcome these challenges, it's essential to establish clear documentation practices, utilize collaborative platforms for workflow management, and schedule regular virtual meetings to ensure alignment. Additionally, maintaining strong version control and automated CI/CD pipelines helps streamline model deployment and monitoring, reducing friction caused by remote coordination. Building proactive communication habits and leveraging cloud-based tools can significantly improve efficiency and team cohesion.

What are the key skills and qualifications needed to thrive as a remote MLOps engineer?

To thrive as an MLOps Engineer, you need a solid background in machine learning, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS or Azure, as well as certifications in cloud services or DevOps, are highly valuable. Strong problem-solving, collaboration, and communication skills help you bridge the gap between data science and operations teams in a remote setting. These competencies are crucial for building scalable, reliable machine learning systems that deliver real-world value efficiently.

What is the difference between Mlops Engineer Remote vs Data Engineer?

AspectMlops Engineer RemoteData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; experience with cloud platforms and ML toolsBachelor's in CS, Data Engineering, or related; strong SQL and ETL skills
Work EnvironmentRemote, collaborative teams, cloud-based infrastructureRemote or on-site, data pipelines, cloud or on-premises systems
Industry UsageTech, AI, ML-focused companiesFinance, healthcare, tech, and other data-driven industries

While both roles involve working with data and cloud platforms, Mlops Engineers focus on deploying and maintaining machine learning models in production, often working remotely with ML-specific tools. Data Engineers primarily build and manage data pipelines and infrastructure. The roles overlap in cloud experience and data handling but differ in their core focus areas.

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Mlops Engineer Remote jobs near you
Infographic showing various Mlops Engineer Remote job openings in the United States as of August 2026, with employment types broken down into 20% Full Time, and 80% Contract. Highlights an 100% Remote job distribution, with an average salary of $115,864 per year, or $55.7 per hour.

Senior AI Engineer (Remote)

Atlanta, GA • On-site, Remote

Home Depot
Retail • 10K+ employees

7.4

Company rating: 7.4 out of 10

Based on 6,476 frontline employees who took The Breakroom Quiz

6th of 39 rated national retailers

People enjoy working here

Good employer

Recommended by students

Learn more about Home Depotexternal

$99K - $136K/yr

Full-time

Medical, Retirement, PTO

Posted 25 days ago

Job description

With a career at The Home Depot, you can be yourself and also be part of something bigger.
Position Purpose:
The Senior AI Engineer is responsible for designing, building, scaling, and optimizing production-grade Agentic AI systems that drive measurable business outcomes across The Home Depot. Operating at the intersection of Data Science, Machine Learning Engineering, and Software Engineering, this hands-on role translates AI concepts into enterprise-ready products.
This role involves developing scalable applications powered by LLMs, SLMs, Retrieval-Augmented Generation (RAG) frameworks, and autonomous agents. You will build the core orchestration layers for multi-agent workflows, tool integration, and planning, alongside the infrastructure required for reliable, large-scale cloud deployment. By partnering with product, engineering, and business teams, you will rapidly prototype solutions, navigate ambiguity, and seamlessly transition cutting-edge AI capabilities from concept to production.
Required skills
  • Experience: 6+ years of experience in AI, Machine Learning Engineering, or Software Engineering with strong Python development skills and modern software engineering practices.
  • AI Delivery: Proven experience building and deploying production-grade AI solutions using LLMs, SLMs, RAG frameworks, copilots, agents, and multi-agent systems.
  • AI Foundations: Deep understanding of AI/ML foundations, including transformers, embeddings, deep learning, prompt engineering, agentic reasoning patterns, and vector databases.
  • Orchestration & Integration: Experience developing orchestration layers (task execution, routing, planning, workflows) and seamlessly integrating AI solutions with enterprise platforms, APIs, and business systems.
  • Infrastructure & MLOps: Expertise in cloud-native architectures, containerization (Docker) and orchestration (Kubernetes/GKE), infrastructure as code (e.g., Terraform), andAI pipeline design, with hands-on implementation of MLOps/LLMOps best practices (CI/CD, automated testing, model versioning and registries, governance, compliance, and security) across the full AI/agent lifecycle.
  • AIOps & Deployment Reliability: Experience building automated CI/CD pipelines for AI/agentic systems, implementing progressive rollout strategies (canary, blue-green, and shadow deployments) with automated rollback, and establishing end-to-end observability (logging, metrics, distributed tracing, and automated alerting) across models, agents, and orchestration layers to ensure production reliability, performance, and cost/token efficiency at scale.
  • Optimization & Debugging: Demonstrated ability to optimize complex AI systems for performance, reliability, scalability, latency, cost efficiency, and token use, as well as debugging operational failure modes.
  • Execution & Collaboration: Excellent cross-functional communication and collaboration skills, with a proven ability to take AI solutions from concept to production in complex enterprise environments.

Key Responsibilities:
  • 70% Delivery and Execution - Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions; Documents, reviews, and ensures that all quality and change control standards are met; Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; Writes custom code or scripts to automate infrastructure, monitoring services, and test cases; Writes custom code or scripts to do "destructive testing" to ensure adequate resiliency in production; Configures commercial off the shelf solutions to align with evolving business needs; Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
  • 10% Learning - Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations
  • 20% Support and Enablement - Fields questions from other product teams or support teams; Monitors tools and participates in conversations to encourage collaboration across product teams; Provides application support for software running in production; Proactively monitors production Service Level Objectives for products; Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality

Direct Manager/Direct Reports:
  • This Position typically reports to Software Engineer Manager or Sr. Software Engineer Manager
  • This Position has 0 Direct Reports

Travel Requirements:
  • Typically requires overnight travel 5% to 20% of the time.

Physical Requirements:
  • Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Working Conditions:
  • Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Minimum Qualifications:
  • Must be eighteen years of age or older.
  • Must be legally permitted to work in the United States.

Preferred Qualifications:
  • Tools & Frameworks: Hands-on experience with Vertex AI, Gemini, Google ADK, LangGraph, CrewAI, AutoGen, or similar orchestration tools and frameworks.
  • AI Infrastructure & Platform Tooling: Hands-on experience with infrastructure-as-code (e.g., Terraform), Kubernetes/GKE for container orchestration, GPU/accelerator provisioning and autoscaling, model registries, feature stores, and vector database operations at production scale.
  • Full-stack skills: Node.js/React/REST, API design, performance optimization, Linux, Git, modern deployment toolchain.
  • Industry Context: Background in retail, supply chain, manufacturing, eCommerce, logistics, or finance where Applied ML is mature.
  • Guardrails & Reliability: Knowledge and experience in establishing Responsible AI, evaluation frameworks, reliability engineering, and AI governance guardrails.
  • Leadership & Innovation: A proven track record of driving innovation, delivering measurable business impact, mentoring engineering teams, and establishing AI engineering standards and best practices.
  • Master's or bachelor's in computer science, Artificial Intelligence, Machine Learning, or a related technical discipline.

Minimum Education:
  • The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.

Preferred Education:
  • No additional education

Minimum Years of Work Experience:
  • 2

Preferred Years of Work Experience:
  • No additional years of experience

Minimum Leadership Experience:
  • None

Preferred Leadership Experience:
  • None

Certifications:
  • None

Competencies:
  • Global Perspective
  • Manages Ambiguity
  • Nimble Learning
  • Self-Development
  • Collaborates
  • Cultivates Innovation
  • Situational Adaptability
  • Communicates Effectively
  • Drives Results
  • Interpersonal Savvy

Benefits offered include health care benefits, 401K, ESPP, paid time off, and success sharing bonus. For a full list of the various benefits The Home Depot offers, visit https://careers.homedepot.com/our-benefits.
For California, Colorado, Connecticut, Rhode Island, Nevada, New York City, Ithaca (NY), Westchester County (NY), and Washington residents:
The pay range for this position is between $100,000.00 - $250,000.00
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About Home Depot

Sourced by ZipRecruiter

The Home Depot is the world’s largest home improvement specialty retailer, operating a vast network of warehouse-format stores across the United States, Canada, and Mexico. Founded in 1978, the company has established itself as the primary resource for building materials, lawn and garden products, and home décor. Its business model caters to two distinct customer bases: Do-It-Yourself (DIY) homeowners and "Pro" customers, such as professional contractors and tradespeople. Beyond product sales, the company offers an extensive suite of services, including professional installation and one of the largest tool rental operations in North America.

Industry

Retail, manufacturing and personal services

Company size

10,000+ Employees

Headquarters location

Atlanta, GA, US

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