ML OPS Engineer

3 weeks ago

Philadelphia, United States Editech Staffing Full time
Editech Staffing does not partner with external agencies, no C2C, no sponsorship

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.

*Completion of I-9, US work elgibility

Hybrid- Philadelphia, Pennsylvania

(Candidates should be located in EST OR CST Time Zone. Need to Travel to the above location 2-3 weeks initially to understand the project after that 3-4 days in a month.)

Our client is seeking a Machine Learning Ops Engineer to join their team You will play a crucial role in building and maintaining the infrastructure and pipelines for our cutting-edge Generative AI applications, working closely with the Generative AI Full Stack Architect . Your expertise in automating and streamlining the ML lifecycle will be instrumental in ensuring the efficiency, scalability, and reliability of our Generative AI models in production.

Job Summary

  • Design, develop, and implement MLOps pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.
  • Automate ML tasks across the model lifecycle, leveraging tools like GitOps, 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 MLOps 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.
Required Qualifications
  • Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
  • 8+ years of experience in MLOps or related areas, such as DevOps, data engineering, or ML infrastructure.
  • Proven experience in automating ML pipelines with tools like MLflow, 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.


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