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Vice President Mlops Engineer Jobs (NOW HIRING)

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JB061761 - MLOps Engineer

Grapevine, TX · On-site

$80/hr

MLOps Engineer Duration: 6 months+, possible extension Rate: $80/hr+, depending on experience ... EVP You are getting to work with several teams that are on the cutting edge of technology for ...

Empire State Realty Trust

VP, Engineering

New York, NY · On-site

$196K - $253K/yr

POSITION SUMMARY The VP, Engineering will report to the SVP, Property Management and is a hands-on, highly visible leader responsible for the reliability, safety, regulatory compliance, and ...

ATAVAS, INC

VP Maritime Engineering

White Plains, NY · On-site

$186K - $240K/yr

Marine Engineering Division, Vice President Reporting to the SVP Operations, this position will be responsible for Business Development, Marketing, Resource Management, and technical oversite of the ...

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Vice President Mlops Engineer information

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How much do vice president mlops engineer jobs pay per year?

As of Jul 17, 2026, the average yearly pay for vice president mlops engineer in the United States is $215,595.00, according to ZipRecruiter salary data. Most workers in this role earn between $180,000.00 and $232,000.00 per year, depending on experience, location, and employer.

How does a Vice President MLOps Engineer typically collaborate with data science, engineering, and business teams?

A Vice President MLOps Engineer plays a pivotal role in bridging the gap between data science, engineering, and business teams. They oversee the deployment and operationalization of machine learning models, ensuring scalability, reliability, and compliance with business goals. Regular collaboration involves aligning model development with infrastructure capabilities, translating business requirements into technical solutions, and guiding teams on best practices for automation and monitoring. Effective communication and cross-functional leadership are essential, as this role often leads initiatives that impact multiple departments and stakeholders.

What is the difference between Vice President Mlops Engineer vs Data Science Manager?

AspectVice President Mlops EngineerData Science Manager
Required CredentialsAdvanced degree in Computer Science, Engineering, or related field; experience with MLOps tools and cloud platformsMaster's or PhD in Data Science, Statistics, or related field; strong programming and analytics skills
Work EnvironmentLeadership role overseeing MLOps teams, collaborating with engineering and data teams, often in enterprise settingsManaging data science projects, leading data teams, and working closely with business units
Industry UsageCommon in tech, finance, and enterprise sectors focusing on deploying ML models at scalePrevalent in research, analytics, and product development teams across various industries

The Vice President Mlops Engineer focuses on deploying and maintaining machine learning operations at an executive level, ensuring scalable and reliable ML systems. In contrast, a Data Science Manager leads data science teams to develop models and insights. Both roles require strong technical backgrounds but differ in strategic scope and leadership responsibilities.

What does a Vice President MLOps Engineer do?

A Vice President MLOps Engineer is a senior leader responsible for overseeing the development, deployment, and management of machine learning operations (MLOps) within an organization. This role involves creating strategies to streamline and automate the lifecycle of machine learning models, ensuring collaboration between data scientists, engineers, and IT teams. They also establish best practices for model monitoring, scalability, and security while aligning MLOps initiatives with business goals. Additionally, they mentor teams, manage resources, and often play a key role in technology selection and vendor management.

What are the key skills and qualifications needed to thrive as a Vice President MLOps Engineer, and why are they important?

To thrive as a Vice President MLOps Engineer, you need deep expertise in machine learning, software engineering, and cloud infrastructure along with a relevant advanced degree. Familiarity with tools like Kubernetes, Docker, CI/CD pipelines, and cloud platforms (AWS, Azure, GCP), as well as certifications in cloud or data engineering, are highly valued. Exceptional leadership, strategic thinking, and communication skills help drive cross-functional collaboration and organizational adoption of MLOps best practices. These capabilities ensure robust, scalable, and efficient deployment of machine learning solutions aligned with business goals.
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Vice President Mlops Engineer jobs near you
SVP, MLOps / DevOps Engineer - Full Time - Hybrid

SVP, MLOps / DevOps Engineer - Full Time - Hybrid

Benchmark IT- Technology Talent

Manhattan, NY • On-site

$180K - $230K/yr

Other

Re-posted 18 days ago

Job description

SVP, MLOps / DevOps Engineer – Permanent – Hybrid
We’re partnering with our client, a fast-growing fintech firm, on a senior-level MLOps-focused hire to help build and scale the infrastructure behind their AI and machine learning platforms.
This is a highly visible, hands-on role where you’ll sit at the intersection of engineering, data, and cloud; helping bring AI/ML models into production in a secure, scalable, and automated way.
The priority here is MLOps first, supported by strong DevOps experience across AWS, Kubernetes (EKS), and GitLab CI/CD.


What You’ll Be Doing

  • Build and scale MLOps pipelines across the full model lifecycle
  • Help productionize AI/ML and GenAI (LLM) workloads
  • Design and manage cloud infrastructure in AWS, leveraging Kubernetes (EKS)
  • Develop and improve CI/CD pipelines (GitLab) for ML and platform services
  • Partner closely with ML engineers to bring models into production
  • Drive automation, reliability, and performance across the platform
  • Implement monitoring, governance, and security for AI workloads

What They’re Looking For

  • 15+ years of experience in MLOps, DevOps, or platform engineering
  • Strong, hands-on MLOps experience in production environments (key priority)
  • Deep experience with AWS (SageMaker preferred)
  • Expertise with Kubernetes/EKS and containerized environments
  • Experience building CI/CD pipelines using GitLab
  • Strong scripting skills (Python or similar) and Infrastructure as Code (Terraform)
  • Exposure to Generative AI / LLMs is a big plus
  • Comfortable working cross-functionally in a fast-paced environment

Why This Role

  • Opportunity to shape and scale AI infrastructure at a growing fintech
  • High-impact, high-visibility role working across teams
  • Strong compensation: $180K–$230K base + bonus + equity
  • Hybrid model: 4 days onsite / 1 day remote (NYC area)

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