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