Responsibilities

• Design, develop and deploy complex AI/ML solutions on cloud infrastructure (using ML engineering, ML Ops workflows & tools) that can scale in response to changing business and technical requirements

• Improvise coding practices, support code reviews and bring in best practices for model management

• Strategize, plan and deliver MLOps initiatives by liaising with key business stakeholders

• Data pipeline Monitoring development & support (operations)
• Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards.
• Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production.
• Build and maintain tools and infrastructure for data processing for AI/ML development initiatives
• Create infra and architecture diagrams

Requirements

Minimum of 5 years of hands-on experience in MLOps with a focus on deploying AI/ML solutions.

• • Experience with ML engineering, model management, supporting code reviews and deploying machine learning models into production environment.
  • Established track record working with AI/MLOps solutions in cloud computing environments including Azure/AWS/GCP
  • Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open source frameworks.
  • Experience with virtualization and cluster management tools, including Docker/Containers, Kubernetes
  • Experience with MLOps tools such as MLFlow and Kubeflow
  • Improving service reliability, observability, develop UI and APIs to improve user experience
  • Experience in Python scripting
  • Experience with CI/CD
  • Fluency in Python data tools e.g. Pandas or Pyspark
  • Experience working on large scale, distributed systems
  • Python/Scala for data pipelines

• Clear written and oral communication skills with a strong desire to share knowledge with clients, partners, and co-workers.