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Aws Mlops Jobs (NOW HIRING)

Chefman

MLOps Engineer / DevOps Engineer

Mahwah, NJ · On-site

$53 - $72.50/hr

Design, implement, and maintain scalable AWS cloud infrastructure supporting software, AI, and machine learning applications. * Create and manage MLOps infrastructure for model training, deployment ...

ClifyX

MLOps Engineer

Hartford, CT · On-site

ClifyX is seeking an MLOps Engineer to enhance their machine learning operations. The primary ... Required : • Work Experience in SageMaker Knowledge in setting up CI/CD aws code pipeline for ...

Vytwo

AI/ML Architect with Databricks , AWS

Prosper, TX · On-site

$60 - $70/hr

AI/ML Architect with Databricks , AWS Remote Role Overview We are seeking an experienced AI/ML ... Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.

Stefanini

MLOps Engineer

Dearborn, MI · On-site

AWS or Google Cloud Platform Docker, Kubernetes API development (FastAPI / Flask) Data pipelines ... Hands-on experience with MLOps tools including MLflow, Airflow, Vertex AI, SageMaker, and Kubeflow.

Vytwo

AI/ML Architect with Databricks , AWS

Prosper, TX · On-site

$150 - $200/hr

Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines ... AWS Cloud Integration * Architect end‑to‑end data and ML solutions using AWS services ...

Redolent, Inc.

Prinicipal MlOps Engineer

Sunnyvale, CA · On-site

Optimize and manage cloud-based ML workloads using AWS, GCP, or Azure, ensuring cost-eJiciency and scalability. * Lead and mentor a team of MLOps engineers, collaborating closely with data scientists ...

Hirekeyz Inc

MLOps Engineer

San Francisco, CA · On-site

MLOps Engineer Location: San Francisco, California Duration: Long Term Contract Key ... Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS ...

smart folks inc

MLOps Developer

Sunnyvale, CA · On-site

MLOPS Ray Developer Location: Austin, TX/ Sunnyvale, CA/ Remote Duration: Long-term * Deep ... Work on multiple cloud environment like AWS and GCP. * Actively participate in capacity planning ...

3B Staffing LLC

MLOps Engineer

Marriottsville, MD · On-site

Job Title: MLOps Engineer Location: Woodlawn, MD (supporting a federal client) local only with ... Manage cloud infrastructure for ML workloads (AWS, Docker, Kubernetes) * Implement monitoring and ...

3B Staffing LLC

MLOps Engineer

Marriottsville, MD · On-site

Job Title: MLOps Engineer Location: Woodlawn, MD (supporting a federal client) local only with ... Manage cloud infrastructure for ML workloads (AWS, Docker, Kubernetes) * Implement monitoring and ...

Sage IT Inc

SRE with MLops Platform

Sunnyvale, CA · On-site

$67 - $89/hr

Python, Kubernetes, Mongo DB, Microservices, AWS * SOLR * ML operations, CI CD pipelines, LLM ... Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud ...

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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 Engineer / DevOps Engineer

Chefman

Mahwah, NJ • On-site

$53 - $72.50/hr

Full-time

Re-posted 16 days ago

Job description

MLOps Engineer / DevOps Engineer

*Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

In 2020, we launched CHEF iQ, an ecosystem of connected kitchen appliances designed to transform how people cook and connect through food. Our mission is to make great cooking effortless through intelligent technology, guided experiences, and seamless integration between hardware, software, and AI. As CHEF iQ continues to expand its AI capabilities, we are building the infrastructure and platforms that will power the next generation of connected cooking experiences. From machine learning and computer vision to Generative AI applications, our success depends on scalable, reliable systems that enable rapid innovation and deployment.
 
We are seeking a highly detail-oriented MLOps / DevOps Engineer to serve as a critical partner to our Machine Learning Engineer, building and maintaining the cloud infrastructure, deployment pipelines, automation frameworks, and operational foundations that support AI development at scale. This individual will play a key role in improving engineering efficiency, increasing system reliability, and ensuring our AI-powered products can be developed, deployed, and scaled successfully.
 
The ideal candidate is passionate about automation, process improvement, and building highly scalable systems. They enjoy creating order from complexity, eliminating operational bottlenecks, and enabling teams to move faster. Experience supporting AI, machine learning, and Generative AI applications in production environments is required.
 
Role and Responsibilities
  • Design, implement, and maintain scalable AWS cloud infrastructure supporting software, AI, and machine learning applications.
  • Create and manage MLOps infrastructure for model training, deployment, monitoring, versioning, and lifecycle management.
  • Partner closely with the Machine Learning Engineer to establish the tools, workflows, and infrastructure required for successful AI development and deployment.
  • Support Generative AI initiatives by building infrastructure and deployment frameworks for applications utilizing AWS Bedrock, foundation models, LLMs, and related AI services.
  • Build and manage Infrastructure as Code (IaC) using Terraform to ensure repeatable, secure, and scalable environments.
  • Implement monitoring, logging, observability, and alerting systems across software, infrastructure, and machine learning platforms.
  • Continuously identify opportunities to improve engineering processes, reduce manual effort, increase automation, and improve system reliability.
  • Develop and maintain CI/CD pipelines that enable rapid, reliable software and machine learning deployments.
  • Optimize cloud environments for scalability, performance, availability, and cost efficiency.
  • Support security, compliance, backup, disaster recovery, and operational best practices across all environments.
  • Troubleshoot infrastructure, deployment, and application issues across development, testing, and production environments.
  • Document infrastructure architecture, deployment processes, operational procedures, and engineering standards.
  • Contribute to establishing best practices for DevOps, MLOps, cloud architecture, and AI operations.
 
Qualifications

Please Note: Chefman is unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

  • 5+ years of experience in MLOps, DevOps, Site Reliability Engineering (SRE), Platform Engineering, or related software engineering roles.
  • Strong hands-on experience with AWS services and cloud-native architecture.
  • Experience building and supporting AI and machine learning platforms in AWS environments.
  • Experience supporting AI, machine learning, and Generative AI applications in production environments.
  • Experience working with AWS Bedrock, Generative AI services, foundation models, LLM-powered applications, or related AI infrastructure.
  • Strong experience implementing Infrastructure as Code using Terraform.
  • Experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Strong experience building and maintaining CI/CD pipelines and deployment automation.
  • Experience supporting machine learning workflows, model deployment, monitoring, and MLOps platforms.
  • Strong programming and scripting skills in Python, Bash, or similar languages.
  • Experience with monitoring, logging, observability, and operational tooling.
  • Strong troubleshooting, systems-thinking, and problem-solving abilities.
  • Excellent communication and cross-functional collaboration skills.
  • Proven track record of improving engineering processes, increasing operational efficiency, and scaling software platforms.
  • Highly organized and detail-oriented with a passion for automation and continuous improvement.
 
Preferred Qualifications
  • Experience with vector databases, retrieval-augmented generation (RAG), model serving, and AI infrastructure.
  • Experience supporting connected devices, IoT platforms, embedded systems, or consumer technology products.
  • Domain expertise in machine learning infrastructure, AI platforms, consumer applications, connected products, or similar technology environments.
  • Experience working in fast-paced startup or high-growth product organizations.

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