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Mlops Manager Jobs in Washington (NOW HIRING)

GIGATEC

MLOps Software Engineer

Annapolis Junction, MD · On-site

$205K - $255K/yr

... management information systems, and systems which incorporate data repositories, data transport services, and application and systems development and monitoring. All being critical to the success of ...

IMPYRIAN

DEVOPS ENGINEER

Ashburn, VA · On-site

$130K - $180K/yr

... , or MLOps roles supporting production systems. * Strong experience designing and managing CI/CD pipelines for software and data/ML workloads. Technical Skills * Hands-on expertise with cloud ...

Impyrian

DEVOPS ENGINEER

Ashburn, VA · On-site

$54 - $74/hr

... , or MLOps roles supporting production systems. * Strong experience designing and managing CI/CD pipelines for software and data/ML workloads. Technical Skills * Hands-on expertise with cloud ...

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Mlops Manager information

What is an MLOps manager?

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.

What are the key skills and qualifications needed to thrive as an MLOps manager?

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 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.

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.

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Mlops Manager jobs near you

AI / ML Engineer Manager

Accenture Federal Services

Arlington, VA • On-site

Full-time

Re-posted 20 days ago

Accenture Federal Services rating

8.7

Company rating: 8.7 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

51st of 500 rated business services

Job description

Job Summary:
Accenture Federal Services is dedicated to helping the US federal government enhance national security and improve lives through technology. As the Manager for the AI/ML Models as a Service team, you will lead a group focused on productionizing machine learning for the DoD, overseeing the development and deployment of AI/ML models to support various missions.
Responsibilities:
• Lead, mentor, and manage a high-performing team of ML modeling developers and MLOps engineers.
• Define and execute the technical strategy for the MaaS platform, including the frameworks for model training, versioning, deployment, and monitoring.
• Oversee the design, development, and deployment of a diverse portfolio of machine learning models to solve complex mission challenges.
• Establish and enforce robust MLOps practices to ensure automated, reliable, and scalable CI/CD pipelines for machine learning models.
• Architect the service layer for the MaaS platform, ensuring models are exposed via secure, scalable, and well-documented APIs.
• Collaborate with data scientists, data engineers, and mission stakeholders to identify use cases and translate requirements into production-ready models.
• Implement governance, security, and ethical AI standards across the entire model lifecycle.
• Manage project timelines, resource allocation, and stakeholder communication for all MaaS initiatives.
Qualifications:
Required:
• 8+ years of experience in data science or machine learning engineering, with at least 3 years in a technical leadership or management role.
• Deep expertise in developing and deploying ML models using common frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Proven experience building and maintaining production ML systems in a cloud environment (AWS, Azure, GCP).
• Strong understanding of MLOps principles and hands-on experience with relevant tools (e.g., MLflow, Kubeflow, AWS SageMaker, Azure ML).
• Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD tools.
• Experience with programming skills in Python and familiarity with software engineering best practices.
• US Citizenship (No Dual Citizenship)
• Active TS or TS/SCI Clearance
Preferred:
• Direct experience building a Model-as-a-Service or Machine-Learning-as-a-Service platform.
• Experience with ML platforms like Databricks or AWS SageMaker AI.
• Familiarity with Infrastructure-as-Code (IaC) tools like Terraform.
• Experience working in a high-security DoD or Intelligence Community environment.
• Demonstrated success leading teams that deliver complex, data-driven software projects.
Company:
Accenture Federal Services is a leading US federal services company and subsidiary of Accenture. Founded in 1989, the company is headquartered in Arlington, USA, with a team of 10001+ employees. The company is currently Late Stage.

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