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Senior Machine Learning Ops Engineer Jobs (NOW HIRING)

CIM Group

Sr. Machine Learning Ops Engineer

Los Angeles, CA · On-site

$112.60K - $154.60K/yr

CIM Group is a community-focused real estate and infrastructure company seeking a Senior ML Ops Engineer to lead the design and maintenance of scalable infrastructure for ML model deployment and ...

Sheetz, Inc

Machine Learning Ops Engineer II

Pittsburgh, PA · On-site

$78.81K - $131.35K/yr

A Machine Learning Ops Engineer II at Sheetz ensures that AI models move seamlessly from "working on a laptop" to running reliably across our stores, applications, and systems at scale. This role ...

Elsevier

Senior ML Ops Engineer

Philadelphia, PA

$107.65K - $171.95K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global ... About the role, as a Senior Machine Learning Engineer you'll work onAI-based features (GenAI ...

RELX

Senior ML Ops Engineer

Philadelphia, PA · On-site

$107.65K - $171.95K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global ... About the role, as a Senior Machine Learning Engineer you'll work onAI-based features (GenAI ...

Elsevier

Senior ML Ops Engineer

Philadelphia, PA · On-site

$107.65K - $171.95K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global ... About the role, as a Senior Machine Learning Engineer you'll work on AI-based features (GenAI ...

RELX Group plc

Senior ML Ops Engineer

Philadelphia, PA · On-site

$107.65K - $171.95K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global ... About the role, as a Senior Machine Learning Engineer you'll work on AI-based features (GenAI ...

Capital One

Senior Machine Learning Engineer

Mclean, VA · On-site

$105.60K - $145.10K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

Capital One

Senior Machine Learning Engineer

Richmond, VA · On-site +1

$103.40K - $142K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

Capital One

Senior Machine Learning Engineer

Chicago, IL · On-site +1

$107.60K - $147.80K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

Capital One

Senior Machine Learning Engineer

Mclean, VA · On-site +1

$105.60K - $145.10K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

Capital One

Senior Machine Learning Engineer

Plano, TX · On-site +1

$100K - $137.30K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137.30K/yr

Senior Machine Learning Engineer Location: Ann Arbor, Michigan Experience Level: 7+ Years ... Strong knowledge of ML Ops practices including version control, model monitoring, and retraining ...

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Senior Machine Learning Ops Engineer information

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$59.5K

$126.6K

$183.5K

How much do senior machine learning ops engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for senior machine learning ops engineer in the United States is $126,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,500.00 and $143,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Ops Engineer, and why are they important?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by Senior Machine Learning Ops Engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What are Senior Machine Learning Ops Engineers?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

More about Senior Machine Learning Ops Engineer jobs
What cities are hiring for Senior Machine Learning Ops Engineer jobs? Cities with the most Senior Machine Learning Ops Engineer job openings:
What are the most commonly searched types of Machine Learning Ops Engineer jobs? The most popular types of Machine Learning Ops Engineer jobs are:
What states have the most Senior Machine Learning Ops Engineer jobs? States with the most job openings for Senior Machine Learning Ops Engineer jobs include:
What job categories do people searching Senior Machine Learning Ops Engineer jobs look for? The top searched job categories for Senior Machine Learning Ops Engineer jobs are:
Senior Machine Learning Ops Engineer jobs near you
Infographic showing various Senior Machine Learning Ops Engineer job openings in the United States as of May 2026, with employment types broken down into 6% As Needed, 22% Full Time, 55% Part Time, and 17% Contract. Highlights an 89% Physical, and 11% Remote job distribution, with an average salary of $126,557 per year, or $60.8 per hour.
Senior Machine Learning Ops Engineer

Senior Machine Learning Ops Engineer

National Debt Relief, LLC.

Remote

$150.50K - $173K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Job description

Overview
National Debt Relief (NDR) is seeking a Senior ML Ops Engineer to help evolve and scale our enterprise machine learning platform. This role sits within the Data Engineering organization on our existing ML Ops team and partners closely with Data Science, Analytics Engineering, and Applied AI teams to productionize machine learning workloads across the company.
Today, many of our models are deployed within Snowflake using containerized FastAPI services and Snowflake-native capabilities. As we continue to mature our ML platform strategy, this role will help design and lead the evolution toward a more flexible cloud-native architecture leveraging AWS and modern ML infrastructure patterns.
You will help own the infrastructure, orchestration, deployment, observability, and reliability of production ML systems. This includes enabling scalable model training and inference workflows, improving developer experience for Data Science teams, and establishing engineering standards for testing, CI/CD, governance, and monitoring.
The ideal candidate combines strong software engineering fundamentals with hands-on ML platform experience across cloud infrastructure, orchestration, containerization, and data systems.
Responsibilities
Essential Duties/Responsibilities:
  • Design, deploy, and maintain scalable ML infrastructure supporting model training, batch inference, and real-time inference workloads.
  • Lead the evolution of model hosting architecture from Snowflake-native services toward cloud-native infrastructure in AWS.
  • Build and maintain containerized model serving solutions using Docker, FastAPI, and modern deployment patterns.
  • Design and manage orchestration workflows for training, retraining, scoring, and inference pipelines using tools such as Dagster, Airflow, Prefect, or similar.
  • Partner closely with Data Science and Analytics Engineering teams to productionize ML models and improve deployment velocity.
  • Build and maintain scalable training and inference datasets using SQL, dbt, and Snowflake.
  • Implement CI/CD, Infrastructure-as-Code, testing, and deployment automation best practices across ML systems and platform infrastructure.
  • Establish observability and monitoring frameworks for deployed ML systems, including model performance monitoring, drift detection, data quality validation, and automated alerting.
  • Optimize platform reliability, scalability, governance, and operational efficiency across ML workflows and supporting infrastructure.
  • Document architecture, deployment standards, and operational processes to support maintainability and reproducibility.

Qualifications
Required Skills & Experience:
  • 5+ years of experience in ML Ops, platform engineering, DevOps, or data platform engineering.
  • Strong Python engineering skills, including API development and automation tooling.
  • Strong experience deploying and operating production machine learning systems.
  • Hands-on experience with cloud infrastructure, preferably AWS.
  • Strong experience with Docker and containerized application deployment.
  • Demonstrated experience building backend services using frameworks such as FastAPI.
  • Strong SQL expertise and experience building production-grade dbt models and data pipelines.
  • Hands-on experience with Snowflake in enterprise production environments.
  • Experience implementing CI/CD workflows and modern software engineering best practices.
  • Experience with orchestration frameworks such as Dagster, Airflow, or Prefect.
  • Experience with pytest testing frameworks and patterns, including unit, integration, and end-to-end testing.
  • Experience with Bash and Unix-based environments.
  • Familiarity with Infrastructure-as-Code tooling such as Terraform.
  • Strong communication and collaboration skills across Data Science, Data Engineering, and Product teams.
  • Ability to operate independently and help define ML platform standards and architecture direction.

Preferred Skills & Experience:
  • Experience deploying ML systems on Kubernetes, ECS, EKS, or other container orchestration platforms.
  • Experience with ML observability and experiment tracking tools such as MLflow, Arize, Evidently, WhyLabs, or Monte Carlo.
  • Experience designing feature stores or reusable ML data products.
  • Experience supporting both batch and low-latency inference workloads.
  • Experience in financial services, fintech, or other regulated industries.
  • Experience supporting Generative AI or LLM deployment workflows.
  • Strong software engineering fundamentals, including design patterns and maintainable architecture practices.

National Debt Relief Role Qualifications:
  • Computer competency and ability to work with a computer.
  • Prioritize multiple tasks and projects simultaneously.
  • Exceptional written and verbal communication skills.
  • Punctuality expected, ready to report to work on a consistent basis.
  • Attain and maintain high performance expectations on a monthly basis.
  • Work in a fast-paced, high-volume setting.
  • Use and navigate multiple computer systems with exceptional multi-tasking skills.
  • Remain calm and professional during difficult discussions.
  • Take constructive feedback.
  • Available for full-time position.

Compensation Information
Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for each position across the US. Within the range, individual pay is determined by work location, job-related skills, experience, and relevant education or training. This good faith pay range is provided in compliance with NYC law and the laws of other jurisdictions that may require a salary range in job postings. The salary for this position is $150,500.00 to $173,000.00.
About National Debt Relief
National Debt Relief was founded in 2009 with the goal of helping an expanding number of consumers deal with overwhelming debt. We are one of the most-trusted and best-rated consumer debt relief providers in the United States. As a leading debt settlement organization, we have helped over 450,000 people settle over $10 billion of debt, while empowering them to lead a healthier financial lifestyle and feel free to live their best life. At National Debt Relief, we treat our clients like real people. Our purpose is to elevate, empower, and transform their lives.
Rated A+ by the Better Business Bureau, our goal is to help individuals and families get out of debt with the least possible cost through conducting financial consultations, educating the consumer and recommending the appropriate solution. We become our clients' number one advocate to help them reestablish financial stability as quickly as possible.
Want to learn more about who we are? Connect with us on social!
Benefits
National Debt Relief is a team-oriented environment full of rewards and growth opportunities for our employees. We are dedicated to our employee's success and growth within the company, through our employee mentorship and leadership programs.
Our extensive benefits package includes:
  • Generous Medical, Dental, and Vision Benefits
  • 401(k) with Company Match
  • Paid Holidays, Volunteer Time Off, Sick Days, and Vacation
  • 12 weeks Paid Parental Leave
  • Pre-tax Transit Benefits
  • No-Cost Life Insurance Benefits
  • Voluntary Benefits Options
  • ASPCA Pet Health Insurance Discount
  • Wellness Incentive Program

National Debt Relief is a certified Great Place to Work®!
National Debt Relief is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other status protected by law.
For information about our Employee Privacy Policy, please see here
For information about our Applicant Terms, please see here
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