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Aws Mlops Jobs (NOW HIRING)

The Avian Consulting LLC

MLOps Engineer

Portland, OR · On-site

MLOps Engineer Location: Portland, OR (Complete Onsite) Note: Client Interview Face to Face Key ... AWS, Azure, or Google Cloud Platform). * Experience with Infrastructure as Code tools such as ...

Icwgroup

MLOps Engineer II

San Diego, CA · On-site

$103K - $141K/yr

Monitor AWS usage, budgets, and cost trends related to ML infrastructure and implement optimization strategies to improve cost efficiency. * Improve automation, reliability, and scalability of ML ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

IVID TEK INC

MLOps Platform Engineer (SageMaker)

Plano, TX · On-site

$90 - $95/hr

MLOps Platform Engineer (SageMaker) Duration : 12 months with extension Location: Onsite in Plano ... on AWS SageMaker Unified Studio. You will migrate the organization from a fragmented ML toolchain ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

Fractal

MLOps Lead

New York, NY · On-site

$112K - $147K/yr

Senior MLOps Engineer Fractal is a strategic AI partner to Fortune 500 companies with a vision to ... Experience with cloud-based technologies (AWS) * Experience building production grade LLM based ...

Delviom LLC

Site Reliability engineer MLops

Sunnyvale, CA · On-site

$67.75 - $90/hr

Design and implement cloud solutions, build MLOps on cloud (AWS or Google Cloud Platform) * Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Flux, Kustomize, Circle CI, Airflow or ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

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Aws Mlops information

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How much do aws mlops jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for aws mlops in the United States is $54.05, according to ZipRecruiter salary data. Most workers in this role earn between $38.70 and $64.42 per hour, depending on experience, location, and employer.

What are common challenges faced by AWS MLOps engineers when deploying machine learning models to production?

AWS MLOps engineers often encounter challenges such as managing model versioning, ensuring seamless integration with CI/CD pipelines, and monitoring model performance after deployment. Handling data drift and automating retraining workflows are also key concerns, as they directly impact model accuracy and reliability. Collaboration with data scientists, DevOps teams, and stakeholders is essential to address these challenges and maintain efficient, scalable machine learning operations in production environments.

What is AWS MLOps?

AWS MLOps refers to the set of practices and tools used to automate, manage, and scale machine learning (ML) workflows on Amazon Web Services (AWS). It combines 'Machine Learning' (ML) with 'Operations' (Ops), enabling teams to streamline the process of building, deploying, monitoring, and maintaining ML models in production. AWS provides specialized services like SageMaker, CodePipeline, and CloudFormation to support these tasks, helping organizations achieve greater efficiency, reliability, and scalability in their ML projects.

What is the difference between Aws Mlops vs Data Scientist?

AspectAws MlopsData Scientist
Required CredentialsCloud certifications (AWS Certified Machine Learning Specialty), programming skills, DevOps knowledgeStatistics, data analysis, programming (Python, R), advanced degrees often preferred
Work EnvironmentCloud platforms, DevOps pipelines, deployment environmentsData analysis, modeling, research environments
Industry UsageTech companies, cloud service providers, organizations deploying ML models at scale

While Aws Mlops focuses on deploying, managing, and automating machine learning models in cloud environments, Data Scientists primarily analyze data, build models, and derive insights. Both roles often collaborate but serve different stages of the ML lifecycle.

What skills and qualifications are needed to thrive as an AWS MLOps engineer?

To thrive as an AWS MLOps Engineer, you need strong foundations in machine learning, cloud computing (especially AWS services), and DevOps practices, typically supported by a degree in computer science or a related field. Familiarity with tools like AWS SageMaker, Lambda, CloudFormation, and CI/CD pipelines, along with certifications such as AWS Certified Machine Learning or DevOps Engineer, is highly valued. Excellent problem-solving, collaboration, and communication skills help bridge the gap between data science and engineering teams. These skills are crucial for efficiently deploying, monitoring, and scaling machine learning models in production environments on AWS.
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What cities are hiring for Aws Mlops jobs? Cities with the most Aws Mlops job openings:
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Aws Mlops jobs near you
Infographic showing various Aws Mlops job openings in the United States as of August 2026, with employment types broken down into 89% Full Time, 1% Part Time, and 10% Contract. Highlights an 79% Physical, 7% Hybrid, and 14% Remote job distribution, with an average salary of $112,422 per year, or $54 per hour.

MLOps Platform Engineer

Interon IT Solutions

Reston, VA • On-site

Contractor

Re-posted 11 days ago

Job description

#W2 only
 
Job title: MLOps Platform Engineer 
Location: Reston VA - In person interviews so need Local In EAST coast only​
 
Description: 
MLOps Platform Engineer 
The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps 
Platform Engineer to design, build, and support enterprise-grade machine learning operations 
capabilities. This role will play a key part in enabling scalable, reliable, and secure ML model 
development and deployment across our cloud and container platforms. 
This is a hands-on engineering role requiring strong expertise in AWS, Kubernetes (EKS), 
CI/CD automation, containerization, and ML platform operations. The ideal candidate will have 
solid engineering fundamentals combined with practical knowledge of ML workflows, 
deployment patterns, and platform reliability. 
Key Responsibilities 
Platform Engineering & Operations  
· Engineer, manage, and support MLOps platform components across AWS and EKS-based 
environments. 
· Oversee deployment, configuration, and operation of infrastructure used for ML training, batch 
inference, and real-time model serving. 
· Ensure platform availability, resilience, and performance across dev, test, and production 
environments. 
· Implement role-based access controls (RBAC), network policies, and scalable namespace 
designs within EKS. 
 
Model Deployment & CI/CD Automation 
· Build and support CI/CD pipelines (GitLab) for model packaging, container image builds, 
vulnerability scanning, and automated deployment flows. 
· Enable standardized model release processes including environment promotion, versioning, and 
rollback workflows. 
· Integrate CI/CD with ML frameworks, model repositories, artifacts, and runtime environments. 
 
Container & Kubernetes Workloads 
· Design and manage EKS workloads supporting containerized ML jobs and microservices. 
· Implement auto-scaling, resource quotas, cluster optimization, and multi-tenant workload 
isolation. 
· Support GPU and CPU-based training/inference workloads. 
 
Monitoring, Observability & Optimization 
· Implement logging, monitoring, and alerting for ML pipelines, model endpoints, batch jobs, 
and platform components. 
· Analyze compute, storage, and data transfer usage to optimize cost efficiency across ML 
workloads. 
· Perform incident response, root cause analysis, and long-term remediation planning. 
 
Collaboration & Enablement 
· Partner with Data Scientists, ML Engineers, and application teams to operationalize end-to-end 
machine learning solutions. 
· Provide technical guidance on best practices for ML model lifecycle management, deployment 
patterns, and scalable architectures. 
· Contribute to documentation, runbooks, onboarding materials, and internal knowledge bases. 
 
--- 
 
Required Qualifications 
· 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, 
CloudWatch, and ECR. 
· Strong experience operating and troubleshooting Kubernetes (preferably AWS EKS). 
· Proficiency in containerization (Docker) and orchestration concepts. 
· Strong programming/scripting experience in Python and Bash. 
· Experience building and managing CI/CD pipelines (GitLab or equivalent). 
· Familiarity with machine learning workflows, including training, inference, and model 
monitoring. 
· Experience with infrastructure-as-code (Terraform or CloudFormation). 
· Experience supporting production platforms, including incident management and root cause 
analysis.