Machine Learning Operations Engineer
Posted 26 Jan 2026
Advertised as “ Machine Learning Operations Engineer (MLOps)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 4+ yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Contract
No salary listed for this role.
Field of studyComputer Science, Data Science, Engineering (or related)
Study levelBachelor's
Machine Learning Operations (MLOps) Engineer
The Team
At Coderoad, we're more than just a software development company—we're your gateway to the global tech world. Whether you're looking to skill up or level up your career, we offer the challenges you’ve been searching for.
We provide end-to-end software development services and give you the opportunity to work on exciting, real-world projects in a supportive environment. Whether it's staff augmentation, dedicated IT teams, or general software engineering, we have opportunities for everyone to challenge themselves and take their career to the next level!
Position Location - Latam (Remote).
Time Zone Requirements - This team operates on the East/West Coast time zones.
About the Role
We are seeking a skilled and innovative Machine Learning Operations (MLOps) Engineer with a focus on Agentic AI to design, deploy, and maintain scalable, robust, and ethical autonomous AI systems. The ideal candidate will combine deep expertise in modern MLOps practices with a solid understanding of agentic AI principles, enabling the seamless integration, monitoring, and optimization of AI models that exhibit autonomous decision-making and adaptability. You will be a key contributor in our cross-functional teams, ensuring our agentic AI solutions are reliable, efficient, and aligned with our business goals and ethical standards.
Key Responsibilities
-
Model Deployment & Integration: Design and implement scalable, secure, and production-grade pipelines for deploying agentic AI models. Focus on seamless integration with existing systems and enable real-time adaptability for autonomous decision-making.
-
Cloud Infrastructure Management: Build and maintain robust cloud infrastructure on Google Cloud Platform (GCP) or Amazon Web Services (AWS) for the entire AI lifecycle. Leverage services like GCP's Vertex AI and Cloud Functions, or their AWS equivalents such as Amazon SageMaker, and AWS Lambda, to create efficient and resilient environments.
-
Automation & CI/CD: Develop and maintain automated workflows for continuous integration, continuous deployment (CI/CD), and continuous training (CT) of agentic AI models. Optimize for performance, scalability, and reliability using CI/CD platforms.
-
Monitoring & Performance Optimization: Implement and manage advanced monitoring systems to track the performance, health, and decision-making accuracy of agentic AI models in production. Utilize specialized tools like Lantrace, AgentOps, or AWS's CloudWatch to detect and resolve issues related to model drift, latency, and bias in real-time.