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Mlops Engineer Jobs in Baltimore, MD (NOW HIRING)

AgileEngine

MLOps Engineer ID72409

Baltimore, MD · On-site

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the ...

AgileEngine, LLC.

MLOps Engineer ID72409

Baltimore, MD · On-site

$140 - $190/hr

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the ...

AgileEngine

MLOps Engineer ID72409

Baltimore, MD · On-site

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the ...

Parsons

Senior Software Developer (MLOps)

Aberdeen, MD · On-site

$58.50 - $77.50/hr

Collaborate with data scientists and ML engineers to productionize models, including feature pipelines, inference services, and monitoring * Implement and maintain end-to-end MLOps workflows ...

GIGATEC

MLOps Software Engineer

Annapolis Junction, MD · On-site

$205K - $255K/yr

The defense community needs an engineering partner who can not only keep up, but bring the technical expertise and passion necessary to solve the new hardest problems -- and do it well. GIGATEC ...

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All JobsMlops Engineer JobsMlops Engineer Jobs in Baltimore, MD

Mlops Engineer information

See Baltimore, MD salary details

$101K

$158.6K

$184K

How much do mlops engineer jobs pay per year?

As of Sep 3, 2026, the average yearly pay for mlops engineer in Baltimore, MD is $158,555.00, according to ZipRecruiter salary data. Most workers in this role earn between $152,295.00 and $170,893.00 per year, depending on experience, location, and employer.

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

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

To thrive as an Mlops Engineer, you need strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What do you need to be a MLOps engineer?

To become a MLOps engineer, you typically need a strong background in software engineering, machine learning, and cloud platforms. Proficiency in programming languages like Python, experience with containerization tools such as Docker, and knowledge of CI/CD pipelines are essential. Certifications in cloud services and familiarity with tools like Kubernetes and ML frameworks also enhance qualifications.

Who earns more, ML engineer or MLOps engineer?

MLOps engineers typically earn slightly more than ML engineers due to their focus on deploying, maintaining, and scaling machine learning systems, which requires expertise in cloud platforms, automation, and infrastructure. Salary differences can vary based on experience, location, and company size, but MLOps roles often command higher compensation because of their specialized skill set.

What are the most commonly searched types of Mlops Engineer jobs in Baltimore, MD?

The most popular types of Mlops Engineer jobs in Baltimore, MD are:

What are popular job titles related to Mlops Engineer jobs in Baltimore, MD?

For Mlops Engineer jobs in Baltimore, MD, the most frequently searched job titles are:

What job categories do people searching Mlops Engineer jobs in Baltimore, MD look for?

The top searched job categories for Mlops Engineer jobs in Baltimore, MD are:

Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Baltimore, MD as of August 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $158,555 per year, or $76.2 per hour.

MLOps Engineer ID72409

AgileEngine

Baltimore, MD • On-site

Full-time

Posted 5 days ago

Job description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE
We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the infrastructure, pipelines, and automation needed to deploy models efficiently at scale. You will implement production monitoring systems, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU compute resources for cost-effective scalability. The role is based onsite in Dallas, TX, and requires close collaboration with data scientists and AI researchers to translate experimental models into production-ready solutions.

WHAT YOU WILL DO
- Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment;
- Build, maintain, and scale the infrastructure, automation, and CI/CD workflows necessary for rapid and efficient model deployment;
- Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability;
- Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production;
- Partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions;
- Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective.

MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering;
- Degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience);
- Engineers located in the US must reside in Dallas, TX, and be willing to work onsite;
- Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring;
- Strong practical experience navigating cloud environments and managing/provisioning GPU compute resources;
- Deep understanding of containerization (e.g., Docker, Kubernetes) and designing robust CI/CD pipelines for automated deployments;
- A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers;
- Upper-intermediate English level.

PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location