Machine Learning Engineer

AgileEngine; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Professional growth

Competitive USD-based compensation

Exciting projects

Flextime

Job summary

AgileEngine in Dallas, Texas is looking for a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to reliable production deployment. You will build and maintain infrastructure, pipelines, and automation to deploy models at scale and monitor performance.

You will implement production monitoring, model versioning, drift detection, and experiment tracking, while managing cloud environments and GPU resources for cost‑effective scalability.

Qualifications

• 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).
• 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.

Responsibilities

• 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.

Skills

MLOps

DevOps

Data Engineering

Software Engineering

Machine Learning

Education

Degree in Computer Science or related field

Tools

Docker

Kubernetes

CI/CD

Experiment Tracking

Model Registry/Versioning

Job description

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

• 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);
• 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;

Perks and Benefits

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps;
• Competitive compensation: We match your ever‑growing skills, talent, and contributions with competitive USD‑based compensation and budgets for education, fitness, and team activities;
• A selection of exciting projects: Join projects with modern solutions development and top‑tier clients that include Fortune 500 enterprises and leading product brands;
• Flextime: Tailor your schedule for an optimal work‑life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.