MLOps Engineer

• Architect and manage our core MLOps infrastructure for model training, validation, and highavailability inference serving.

• Develop and own our CICDCT (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.

• Minimum Qualifications

  • 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.

  • Handson 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 CICD pipelines from scratch (e.g., using Jenkins, GitHub Actions).

  • Preferred Qualifications

    • MS in Computer Science or a related technical field.

    • Proficient in Python, with experience writing scientific software and collaborating in codecentric research environments.

    • Deep familiarity with AWS and Terraform codified VPCs, EKS clusters, IAM leastprivilege policies, and multiaccount 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 GPUlevel metrics and build realtime dashboards tracking utilization, memory, error rates, drift, and latency across training and inference.

    • Strong grasp of the endtoend machine learning lifecycle, from data ingestion and processing through model training, evaluation, deployment, and monitoring.

    • Experience working with humancentered, complex, and often messy datasets, with domain knowledge in social sciences or adjacent fields such as behavioral research, humancomputer interaction, or digital media.