Posted on March 11, 2024

  • Design, develop, and implement ML Ops pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.
  • Automate ML tasks across the model lifecycle, leveraging tools like Git Ops, CI/CD pipelines, and containerization technologies (e.g., Docker, Kubernetes).
  • Develop and maintain robust monitoring and alerting systems for generative AI models in production, ensuring proactive identification and resolution of issues.
  • Collaborate with the Generative AI Full Stack Architect and other engineers to optimize model performance and resource utilization.
  • Manage and maintain cloud infrastructure (e.g., AWS, GCP, Azure) for ML workloads, ensuring cost-efficiency and scalability.
  • Stay up to date on the latest advancements in ML Ops and incorporate them into our platform and processes.
  • Communicate effectively with technical and non-technical stakeholders about the health and performance of generative AI models.

ML Ops Engineer

Pennsylvania, NYC, Toronto – USA

Need to Travel to any of these locations 2-3 weeks initially to understand the project after 3-4 days in a month.


  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
  • 8+ years of experience in ML Ops or related areas, such as DevOps, data engineering, or ML infrastructure.
  • Proven experience in automating ML pipelines with tools like ML flow, Kubeflow, Airflow, etc.
  • Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads.
  • Strong understanding of CI/CD principles and containerization technologies like Docker and Kubernetes.
  • Familiarity with monitoring and alerting tools for ML systems (e.g., Prometheus, Grafana).
  • Excellent communication, collaboration, and problem-solving skills.
  • Ability to work independently and as part of a team.
  • Passion for Generative AI and its potential to revolutionize various industries.
  • Senior individual contributor with significant expertise and leadership experience.
  • Manages complex projects and initiatives with independent decision-making authority.
  • Provides technical guidance and mentoring to junior team members.
  • Has a proven track record of success in delivering impactful results.