MLOps Engineer

Summary

Join Reality Defender, a leading AI-generated media detection company, as an MLOps Engineer. You will architect and manage core MLOps infrastructure, develop CI/CD/CT pipelines, implement monitoring and alerting systems, and ensure security best practices. Collaborate with AI and Engineering teams to streamline workflows and accelerate value delivery. This role requires a BS in Computer Science or related field, 3+ years of experience in MLOps, DevOps, or Software Engineering, and proficiency in cloud platforms, containerization, and CI/CD pipelines. Preferred qualifications include an MS in Computer Science, Python proficiency, AWS and Terraform expertise, and experience with ML workflow orchestration tools.

Requirements

• BS in Computer Science, a related technical field, or equivalent practical experience
• 3+ years of professional experience in an MLOps, DevOps, or Software Engineering role with a focus on infrastructure
• Hands-on experience with at least one major cloud provider (e.g., AWS, GCP, Azure)
• Strong proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes)
• Demonstrated experience designing and implementing automated CI/CD pipelines from scratch (e.g., using Jenkins, GitHub Actions)

Responsibilities

• Architect and manage our core MLOps infrastructure for model training, validation, and high-availability inference serving
• Develop and own our CI/CD/CT (Continuous Integration, Delivery, and Training) pipelines to automate the testing and deployment of ML models
• Implement comprehensive monitoring and alerting for model performance, data drift, and system health to guarantee production stability and uptime
• Implement and maintain security best practices throughout the ML lifecycle, including data privacy, access management, and infrastructure hardening, in close collaboration with security and engineering teams
• Partner closely with the AI and Engineering teams to streamline workflows, remove bottlenecks, and empower them to deliver value faster

Preferred Qualifications

• MS in Computer Science or a related technical field
• Proficient in Python, with experience writing scientific software and collaborating in code-centric research environments
• Deep familiarity with AWS and Terraform - codified VPCs, EKS clusters, IAM least-privilege policies, and multi-account landing zones are second nature to you
• Comfortable with ML workflow orchestration and metadata tools such as MLflow or Airflow, and experienced in Linux system administration
• Skilled in configuring monitoring and observability platforms like Weights & Biases or Datadog, with the ability to integrate GPU-level metrics and build real-time dashboards tracking utilization, memory, error rates, drift, and latency across training and inference
• Strong grasp of the end-to-end machine learning lifecycle, from data ingestion and processing through model training, evaluation, deployment, and monitoring
• Experience working with human-centered, complex, and often messy datasets, with domain knowledge in social sciences or adjacent fields such as behavioral research, human-computer interaction, or digital media