Description

You will scale proof-of-concept products into enterprise-grade applications, ensuring reliability, scalability, monitoring, and security. You own the deployment, lifecycle management, and monitoring of Machine Learning and Deep Learning models through all production stages.

Responsibilities

• Automate and streamline ML operations to improve data scientist and engineer productivity.
• Implement software engineering best practices across ML workflows to ensure CI/CD, reproducibility, and rapid delivery cycles.
• Build and maintain tools for deployment, monitoring, and operations while troubleshooting issues across development, testing, and production environments.
• Collaborate with cloud platform and application engineers to implement cloud policies and governance for model lifecycles.
• Lead the end-to-end lifecycle management of ML and Deep Learning models in production.

Required Skills

• 8 to 10 years of industry experience in Software Engineering, DevOps, or Data Engineering.
• Hands-on experience with MLOps tools including DataIKU, ModelDB, Kubeflow, Pachyderm, or Data Version Control (DVC).
• Experience with AWS, Databricks, and Azure Kubernetes Service.
• Proficiency in distributed computing, data pipelines, and AI/ML workflows.
• Extensive experience working in Unix, AIX, or Linux environments.
• Experience with automation servers such as Jenkins, CloudBees, Travis, or GitLab Actions.
• Experience with logging tools including Splunk, ElasticSearch, Kibana, or Logstash.

Preferred Skills

• Familiarity with hyperparameter tuning tools like AWS SageMaker or Kubeflow.
• Knowledge of model and experiment versioning technologies like MLFlow or AWS SageMaker.

Key Skills

Education

Any Graduate