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Mlops Manager Jobs (NOW HIRING)

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

Key Responsibilities Platform Engineering & Operations · Engineer, manage, and support MLOps platform components across AWS and EKS-based environments. · Oversee deployment, configuration, and ...

Apply Early

Apptronik

Staff MLOps Engineer

Austin, TX

This is a hands-on technical leadership role, not a management position; you will be a primary ... Own the technical direction for the MLOps platform - define subsystem interfaces, drive ...

Stefanini

MLOps Engineer

Dearborn, MI · On-site

Establish and maintain MLOps practices, including automated training, deployment, monitoring, retraining, and performance management. Ensure AI solutions are reliable, scalable, secure, and optimized ...

Stanford Health Care

DevOps Engineer

Newark, NJ · Remote

$79.21 - $104.97/hr

You won't just manage servers; you will build the robust, full-stack "factory" where multi-agent ... A Brief Overview The MLOPs Engineer will play an integral role incorporating Artificial ...

Tech Tammina LLC

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA Required Qualifications · 3+ years of hands-on ... managing CI/CD pipelines (GitLab or equivalent). · Familiarity with machine learning workflows ...

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Apptronik

Staff MLOps Engineer

Austin, TX · On-site

This is a hands-on technical leadership role, not a management position; you will be a primary ... Own the technical direction for the MLOps platform - define subsystem interfaces, drive ...

Kapitus

MLOps Architect - Gen Al

Washington, DC · On-site

MLOps & GenAI Platform Architecture * Design and implement scalable ML and LLM infrastructure on ... Managed services (e.g., SageMaker endpoints, Bedrock-style APIs) * Containerized custom inference ...

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CTC USA, LLC

MLops Engineer

Plano, TX · On-site

Manage SageMaker Model Registry -- cross-account model promotion, versioning, immutability, and ... MLOps pipelines -- training, versioning, deployment, monitoring, rollback * Experience with ...

Kapitus

MLOps Architect - Gen Al

Arlington, VA · On-site

We are seeking a senior MLOps Architect to design and scale a modern ML and Generative AI platform ... Managed services (e.g., SageMaker endpoints, Bedrock-style APIs) * Containerized custom inference ...

Stanford Health Care

MLOps Engineer

Newark, CA · On-site

You won't just manage servers; you will build the robust, full-stack "factory" where multi-agent ... A Brief Overview The MLOPs Engineer will play an integral role incorporating Artificial ...

Tech Tammina

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA Required Qualifications • 3+ years of hands-on ... Preferred Qualifications • Experience managing Data Analytics Platforms / Tools (e.g., Domino ...

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Mlops Manager information

What engineer makes $500,000 a year?

Senior machine learning engineers and MLOps managers with extensive experience, advanced skills in cloud platforms, and expertise in deploying scalable AI systems can earn $500,000 or more annually. High compensation often reflects leadership roles, specialized knowledge, and working in high-demand industries or companies with competitive benefits.

What is the difference between Mlops Manager vs Data Scientist?

AspectMlops ManagerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; certifications in cloud platforms or MLOps toolsBachelor's/Master's in CS, Statistics, or related; certifications in data analysis or machine learning
Work EnvironmentCollaborates with engineering, DevOps, and data teams to deploy and maintain ML systemsAnalyzes data, builds models, and provides insights to inform business decisions
Employer & Industry UsageTech companies, AI startups, enterprises implementing ML pipelinesResearch institutions, tech firms, finance, healthcare, and marketing sectors

The Mlops Manager focuses on deploying, maintaining, and optimizing machine learning systems within an organization, working closely with engineering and DevOps teams. In contrast, a Data Scientist primarily analyzes data, develops models, and provides insights. While both roles require knowledge of machine learning, the Mlops Manager emphasizes operationalizing ML solutions, whereas the Data Scientist emphasizes data analysis and modeling.

What are the key skills and qualifications needed to thrive as an MLOps Manager, and why are they important?

To thrive as an MLOps Manager, you need expertise in machine learning, software engineering, and DevOps practices, often backed by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, Azure, GCP), and certifications such as AWS Certified Machine Learning or Google Cloud Professional ML Engineer are highly beneficial. Strong leadership, problem-solving, and cross-functional communication skills help manage teams and bridge the gap between data science and IT operations. These abilities are crucial for ensuring reliable, scalable, and efficient deployment of machine learning solutions in production environments.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as an AI executive, senior machine learning engineer, or AI research director, often requiring advanced skills, extensive experience, and leadership responsibilities. These roles may involve overseeing AI strategy, developing complex models, and managing teams, with compensation reflecting the seniority and impact of the position.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and their role is unlikely to be fully replaced by AI. Instead, AI tools can augment their work by automating routine tasks, allowing MLEs to focus on complex problem-solving, model optimization, and system integration. Continuous learning and expertise in AI frameworks and programming are essential for MLEs to stay relevant in evolving technological environments.

What are some common challenges an MLOps Manager faces when integrating machine learning models into production environments?

MLOps Managers often encounter challenges such as ensuring seamless collaboration between data science and engineering teams, managing model versioning, and maintaining reliable deployment pipelines. Balancing rapid experimentation with the need for robust, scalable, and secure production systems can be complex. Additionally, monitoring model performance post-deployment and handling data drift or model degradation are ongoing responsibilities. Effective communication and establishing standardized processes are key to overcoming these challenges and ensuring successful model operations.

Is MLOps in high demand?

MLOps managers are in high demand due to the increasing adoption of machine learning and AI across industries. Organizations seek professionals skilled in deploying, monitoring, and maintaining ML models using tools like Kubernetes, Docker, and cloud platforms, making MLOps a rapidly growing field with strong job prospects.

What are MLOps Managers?

MLOps Managers are professionals responsible for overseeing the deployment, operation, and scaling of machine learning models in production environments. They coordinate teams to ensure seamless collaboration between data scientists, engineers, and IT staff, facilitating the automation of machine learning workflows. Their role involves managing infrastructure, optimizing processes for model monitoring and maintenance, and ensuring compliance with organizational and industry standards. MLOps Managers play a key role in bridging the gap between model development and operationalization, ensuring that machine learning solutions are reliable, reproducible, and scalable.
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Mlops Manager jobs near you

MLOps Platform Engineer

Interon IT Solutions

Reston, VA • On-site

Contractor

Posted 17 hours ago

Be an early applicant

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.