ML Ops Engineer

ML Ops Engineer

Location : Pennsylvania NYC Toronto.

Candidates should be in EST Time Zone.

Need to Travel to any of these locations 23 weeks initially to understand the project after 34 days in a month.

Summary:

We are seeking a highly skilled and experienced MLOps Engineer to join our team in USA. You will play a crucial role in building and maintaining the infrastructure and pipelines for our cuttingedge 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.

Responsibilities:

• Design develop and implement MLOps pipelines for generative AI models encompassing data ingestion preprocessing 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 costefficiency and scalability.
• Stay uptodate on the latest advancements in MLOps and incorporate them into our platform and processes.
• Communicate effectively with technical and nontechnical stakeholders about the health and performance of generative AI models.

Qualifications:

• Bachelors 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 problemsolving skills.
• Ability to work independently and as part of a team.
• Passion for Generative AI and its potential to revolutionize various industries.

Band 4C:

• Senior individual contributor with significant expertise and leadership experience.
• Manages complex projects and initiatives with independent decisionmaking authority.
• Provides technical guidance and mentoring to junior team members.
• Has a proven track record of success in delivering impactful results.

Machine Learning,CI/CD,aws,azure,gcp