ZipRecruiter

Log In

1

Mlops Manager Jobs (NOW HIRING)

HCA Healthcare

Staff MLOps Engineer

Murfreesboro, TN · On-site

Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform. Optimize for scalability, security, cost-effectiveness, and ...

HCA Healthcare

Staff MLOps Engineer

Lebanon, TN · On-site

Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform. Optimize for scalability, security, cost-effectiveness, and ...

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA - In person interviews so need Local In EAST coast only ... Container & Kubernetes Workloads · Design and manage EKS workloads supporting containerized ML ...

HCA Healthcare

Staff MLOps Engineer

Franklin, TN · On-site

Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform. Optimize for scalability, security, cost-effectiveness, and ...

HCA Healthcare

Staff MLOps Engineer

Nashville, TN · On-site

Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform. Optimize for scalability, security, cost-effectiveness, and ...

E-Solutions, Inc.

Agentic AI/MLOps Engineer

San Jose, CA · On-site

Agentic AI/MLOps Engineer Location: San Jose, CA - On-Site Duration: Long-Term Contract - W2 We are ... Design, build, and manage automated data ingestion, transformation, and validation pipelines using ...

KAPI LLC

Be Seen First

Senior MLOps Engineer - Snowflake

Dallas, TX · On-site

$100K - $130K/yr

Establish model registry, versioning, lineage, artifact management, and reproducibility ... Develop reusable MLOps frameworks, standards, templates, and best practices. Mandatory ...

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 ...

Parsons

Senior Software Developer (MLOps)

Aberdeen, MD

$58.50 - $77.50/hr

Design and implement data and model-serving services that integrate machine learning components into operational C-UAS workflows MLOps, ML Integration & Lifecycle Management * Collaborate with data ...

Atomic Machines

MLOps Engineer

Emeryville, CA · On-site

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data ... management. * Experience with S3, lakehouse technologies such as Apache Iceberg, and workflow ...

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 ...

Marlabs

Computational Biology MLOps Engineer

San Diego, CA · On-site

$118K - $139K/yr

Computational Biology MLOps Engineer | About You As a Computational Biology MLOps Engineer, you are ... Hands on Kubernetes experience in deploying and managing containerized ML workloads with ...

Tiger Analytics Inc.

MLOps Lead Engineer

Saint Louis, MO · On-site

$99K - $131K/yr

We are seeking an experienced MLOps Lead Engineer to take full ownership of our enterprise Machine ... Stakeholder Management: Proven track record in a client-facing technical lead role, with strong ...

Atomic Machines

MLOps Engineer

Emeryville, CA · On-site

We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data ... management. * Experience with S3, lakehouse technologies such as Apache Iceberg, and workflow ...

previous pagenext page

Showing results 21-40

All JobsTechnology JobsSoftware JobsSoftware Development Manager JobsMlops Manager Jobs

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.

More about Mlops Manager jobs

What cities are hiring for Mlops Manager jobs?

Cities with the most Mlops Manager job openings:

What are the most commonly searched types of Mlops jobs?

The most popular types of Mlops jobs are:

What states have the most Mlops Manager jobs?

States with the most job openings for Mlops Manager jobs include:

What job categories do people searching Mlops Manager jobs look for?

The top searched job categories for Mlops Manager jobs are:

Mlops Manager jobs near you
Infographic showing various Mlops Manager job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, 12% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

MLOps Engineer (Databricks/AWS)

Inabia Solutions and Consulting, Inc.

Columbus, OH • On-site

Contractor

Re-posted 14 days ago

Job description

Overview
Inabia is seeking an MLOps Engineer (Databricks/AWS) to design, develop, and maintain end-to-end machine learning operations pipelines on Databricks running on AWS. This role demands deep hands-on expertise with Databricks Machine Learning, MLflow, and a broad suite of AWS services to build scalable, production-grade ML systems. The ideal candidate will partner closely with Data Scientists, Data Engineers, and Platform Engineering teams to productionize models and ensure robust, governed, and cost-efficient deployments.
Locations: Columbus, OH - Dallas, TX - Atlanta, GA (Onsite only)
Responsibilities
  • Design, develop, and maintain end-to-end MLOps pipelines on Databricks running on AWS.
  • Build and automate machine learning workflows covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
  • Deploy and manage ML models using MLflow Model Registry and Databricks Model Serving.
  • Develop and maintain CI/CD pipelines for ML solutions across development, staging, and production environments.
  • Collaborate with Data Scientists to productionize machine learning models and ensure reliable, repeatable deployments.
  • Monitor model health, prediction quality, data drift, and system performance, and implement automated retraining strategies where required.
  • Optimize Databricks workloads for performance, scalability, and cost efficiency.
  • Implement Infrastructure as Code using Terraform to provision and manage Databricks and AWS resources.
  • Ensure platform security, governance, and compliance using Unity Catalog and AWS IAM.
  • Troubleshoot production issues, perform root cause analysis, and continuously improve platform reliability.
  • Document MLOps processes, deployment standards, and operational best practices.

Key Qualifications
  • 10+ years of relevant experience in MLOps or a closely related data/ML engineering discipline.
  • Strong hands-on experience with Databricks Machine Learning.
  • Proficiency in Python, PySpark, and SQL for developing and operationalizing machine learning solutions.
  • Hands-on experience with MLflow for experiment tracking, model registry, model versioning, and lifecycle management.
  • Experience deploying and managing machine learning models using Databricks Model Serving and batch inference pipelines.
  • Strong understanding of the end-to-end ML lifecycle, including feature engineering, training, validation, deployment, monitoring, and retraining.
  • Experience building scalable ML pipelines using Databricks Workflows and Delta Lake.
  • Hands-on experience with AWS services including S3, IAM, EC2, Lambda, ECR, ECS/EKS, CloudWatch, and Secrets Manager.
  • Experience implementing CI/CD pipelines for Databricks and ML workloads using Git, Bitbucket, Jenkins, and Databricks Asset Bundles (DAB).
  • Experience with infrastructure automation using Terraform.
  • Strong understanding of Apache Spark architecture, optimization, and distributed data processing.
  • Experience working with Unity Catalog for governance, security, and access management.
  • Knowledge of model monitoring, data drift detection, and automated retraining strategies.
  • Understanding of MLOps best practices including reproducibility, versioning, testing, and governance.
  • Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications
  • Experience with multi-environment Databricks deployments spanning development, staging, and production.
  • Familiarity with cost optimization strategies for large-scale Databricks and AWS workloads.
  • Prior experience documenting MLOps operational standards for cross-functional teams.

Apply