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

  • 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

  • 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

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Hiring process timeline

We designed our hiring process to be fast, transparent, and convenient — so you always know what to expect and can move through the steps without unnecessary delays.

lightning-iconA quick note

After applying, keep an eye on your inbox — including your spam folder. Occasionally, emails from our hiring platform, LaunchPod, may land there.

1

Tell us about yourself

Submit a short application form.

2

Pass a quick test

Pass a 30’- 60’ test.

3

Record a short video

Introduce yourself on video to fast-track your application process.

4

Meet our team

Once you pass the video review, grab a spot on our calendar for a technical interview.

5

Get an offer

Welcome to AgileEngine!

FAQ

Have any questions?

What is the work format and schedule?

We are a remote-first company, so all our positions are remote. Schedules are flexible, but the main requirement is to have an overlap with your client’s schedule to ensure smooth collaboration. Specific details depend on the project and are discussed during the interview process.

What level of English is required?

We look for Upper-Intermediate (B2) proficiency or higher. Since you will be collaborating with international teams and global clients (including Fortune 500 companies), English is a part of your daily work.

Does AgileEngine provide work equipment?

It depends on your location. We provide equipment in Ukraine, Poland, Argentina, Colombia, Mexico, Brazil, Guatemala, Portugal, Spain, and India. In the USA, equipment is usually provided by the client; otherwise, you will be expected to use your own setup.

What opportunities for professional growth do you offer?

We support continuous growth through personalized development paths tailored to each expert. Also, you can expect:

  • An annual learning and development budget
  • Internal workshops, tech talks, and mentorship programs
  • Opportunities to switch projects or grow into new roles over time

What does a typical team look like, and what tools do you use?

Teams vary by project, but you’ll usually work with a mix of experts across different fields, along with a delivery or project manager who supports collaboration and onboarding.

For day-to-day work, teams commonly use tools like Jira and Google Workspace, along with communication platforms such as Google Chat. Depending on the client, you may also work with Slack, Confluence, Notion, or similar tools.

Need help?

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If something doesn’t work or you have questions regarding your application process, drop us a line.

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