Machine Learning Operations Engineer (MLOps)

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).

Requirements

- This team operates on the East/West Coast time zones.

, implementing safeguards for data privacy, bias mitigation, and transparency in autonomous operations. Collaboration: Work closely with AI researchers, data scientists, software engineers, and product teams to align MLOps processes with project goals. Facilitate iterative development and deployment of agentic AI solutions.

Data Model Governance: Establish and enforce robust data and model governance frameworks, ensuring data quality, security, and compliance with industry standards for all agentic AI systems.

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. Security Compliance: Integrate security best practices throughout the MLOps lifecycle.

Qualifications

Experience: 4+ years of experience in MLOps, DevOps, or a related field, with at least 1 year focused on deploying and managing AI/ML models in production. Experience with agentic or autonomous AI systems is highly preferred. Cloud Expertise: (4years)Deep hands-on experience with either Google Cloud Platform (GCP) or Amazon Web Services (AWS).

Knowledge of relevant services such as GCP's Vertex AI, Cloud Storage, BigQuery, and Cloud Functions or AWS equivalents like Amazon SageMaker, S3, Redshift, and Lambda. Technical Stack: (1 year or less)Strong knowledge of MLOps tools and frameworks(Pytorch, Langraph, CrewAI, N8N). Proficiency in containerization with Docker and orchestration with Kubernetes.