About the role

We are looking for an MLOps Engineer to deploy, scale, and monitor computer vision models in production on Azure, processing real-time image streams from thousands of edge cameras.

The mandatory requirements are being based in Dallas, TX, or open to relocating there, availability to work onsite 3 days a week, production model serving with Triton Inference Server, TorchServe, KServe, or Azure ML endpoints, hands-on experience with computer vision or image data in production, Azure (AKS and Azure DevOps), 3+ years of experience in MLOps, DevOps, ML, or software engineering, Docker, Kubernetes, CI/CD, and an upper-intermediate English level.

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).
  • Must be based in Dallas, TX, or open to relocating to Dallas. Office attendance: 3 days/week.
  • MLOps Core: Production model serving (Triton Inference Server, TorchServe, KServe, or Azure ML endpoints) behind REST/gRPC APIs, with end-to-end backend pipeline ownership. Exposure to tracking, model registry/versioning, drift detection, and production telemetry/monitoring.
  • Computer Vision: Working exposure to CV/image data in production.
  • Infrastructure & Cloud: Strong practical experience navigating cloud environments, strictly focused on Azure, specifically using AKS for deployment, Azure DevOps, and the Azure data stack, alongside managing/provisioning GPU compute resources.
  • DevOps & Automation: Deep understanding of containerization (e.g., Docker and Kubernetes) and designing robust CI/CD pipelines for automated deployments.
  • AI/ML Foundation: A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers.
  • Upper-intermediate English level.

What you will do

  • Resource & Cost Optimization: Lead GPU compute efficiency and infrastructure cost-reduction strategies by optimizing models to maximize camera throughput per GPU. Apply optimization techniques including quantization (FP16/INT8), pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
  • End-to-End Deployment: Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment.
  • Infrastructure & Pipelines: Build, maintain, and scale robust ingestion and inference pipelines for high-volume image streams using Azure Data Factory, cloud storage, and automated CI/CD workflows.
  • Edge & Cloud Deployment: Architect and manage large-scale cloud-based model inference environments, with secondary support for edge GPU deployment across specific edge use cases.
  • System Stability & Monitoring: Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability.
  • Model Lifecycle Management: Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production.
  • Cross-Functional Collaboration: Partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions.

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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