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Mlops Engineer Remote Jobs (NOW HIRING)

FortifyIQ

Data & AI Engineer (Remote)

Salem, MA · Remote

$125K - $150K/yr

We're seeking a Data & AI Engineer to develop intelligent data pipelines and analytics solutions ... Knowledge of MLOps principles (deployment, monitoring, CI/CD)

Rula Health

Principal AI Engineer (Remote)

$280K - $330K/yr

... MLOps, data pipelines, evaluation, and observability systems for continuous model improvement ... Experience leading or mentoring engineering teams in AI or ML platform domains. We're serious about ...

Kasmo Global

MLOPS Ray Developer

$70/hr

MLOPS Ray Developer Location ... Austin, TX/ Sunnyvale, CA/ Remote Rate: $70/hr. - pls don't hold good profiles because of rate.

Sedgwick

Senior Engineer - LLMOps & MLOps

North East, PA · On-site +1

$96K - $132K/yr

... Engineer - LLMOps & MLOps Role Overview This is a high-stakes, execution-focused role within the ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...

Sedgwick

Senior Engineer - LLMOps & MLOps

Minto, AK · On-site +1

$108K - $148K/yr

... Engineer - LLMOps & MLOps Role Overview This is a high-stakes, execution-focused role within the ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...

Sedgwick

Senior Engineer - LLMOps & MLOps

Los Angeles, CA · On-site +1

$112K - $154K/yr

... Engineer - LLMOps & MLOps Role Overview This is a high-stakes, execution-focused role within the ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...

York Risk Services

Senior Engineer - LLMOps & MLOps

Minto, AK · On-site +1

$108K - $148K/yr

... Engineer - LLMOps & MLOps Role Overview This is a high-stakes, execution-focused role within the ... remote #LI-TS1 Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...

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Mlops Engineer Remote information

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$38K

$115.9K

$191.5K

How much do mlops engineer remote jobs pay per year?

As of Jun 27, 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 are some 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 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.

What does an MLOps Engineer do, especially 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 the key skills and qualifications needed to thrive as an MLOps Engineer (Remote), and why are they important?

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.
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Mlops Engineer Remote jobs near you
Infographic showing various Mlops Engineer Remote job openings in the United States as of June 2026, with employment types broken down into 94% Full Time, and 6% Part Time. Highlights an 37% Physical, 3% Hybrid, and 60% Remote job distribution, with an average salary of $115,864 per year, or $55.7 per hour.
Lead Instructor: MLOps / AI Platform Engineering

Lead Instructor: MLOps / AI Platform Engineering

General Assembly

OR • Remote

$11K - $15K/wk

Other

Posted 25 days ago

Job description

Job Title: Lead Instructor: MLOps / AI Platform Engineering

Company: General Assembly

Client: Confidential - Customer Success Reskilling

Location: Remote (Must work West Coast / Pacific Time hours)

Duration: 2 Weeks (Starting Mid-June)

Commitment: Roughly 30 hours per week

Compensation: $11,500 - $15,500 (Estimated lump sum payment for one 60-hour program)

About the Engagement

General Assembly is delivering a specialized reskilling program designed to transition Customer Success and Account Management professionals into MLOps and AI Platform Engineering roles.

As the Lead Instructor, you will be the face of lessons. You aren't just checking the math; you are bridge-building. You will lead experienced customer-facing professionals through the complexities of taking AI systems from pilot to production, ensuring they leave the 2-week intensive with a functional understanding of MLOps governance and deployment.

What You'll Do
  • Lead Live Instruction: Deliver high-energy, synchronous remote lectures and "prompt-along" sessions covering ML pipelines, model deployment, and monitoring.
  • Simplify the Complex: Translate high-level MLOps concepts (CI/CD for ML, governance frameworks) into digestible insights for learners who are experienced professionals but not career engineers.
  • Facilitate Hands-on Labs: Guide students through self-paced exercises and live troubleshooting within the Azure AI Foundry and ML environments.
  • Mentor & Office Hours: Provide real-time feedback during dedicated lab hours, helping students navigate technical roadblocks in Python and Azure infrastructure.
  • Drive Learning Outcomes: Ensure students can successfully articulate and execute model lifecycle management strategies by the end of the cohort.
What You Bring
  • The Experience: 7+ years in software or data engineering, with at least 3+ years specifically in MLOps or ML platform roles in a production environment.
  • The "Teacher" Gene: Proven experience in technical instruction, bootcamp delivery, or corporate training. You should be comfortable "reading the room" in a virtual setting.
  • Azure Fluency: Deep, hands-on expertise with Azure ML and AI Foundry. You should be able to navigate these platforms in your sleep.
  • Technical Foundation: Proficiency in Python, Data Engineering fundamentals, and applying DevOps/CI/CD principles specifically to ML workloads.
  • The Credentials: AZ-900, AI-900, and DP-100 are required; AI-102 is preferred.
  • The Pedigree: Experience as an AI Platform or Azure ML engineer at a major tech firm (Microsoft, Google, etc.) is a massive plus.
Note on Schedule: This is a high-intensity, 2-week engagement. Candidates must be fully available for 30 hours per week during the mid-June window and be prepared to operate on Pacific Time (PT) schedules to align with the learner cohort.

Apply