Description

You will design and lead the development of core components for a machine learning platform, focusing on scalability, reliability, and cloud-agnostic principles.

Responsibilities

• Build APIs and microservices powering end-to-end machine learning workflows, including model deployment, orchestration, and observability.
• Own cloud infrastructure and CI/CD automation using Infrastructure-as-Code principles for reliable deployment pipelines.
• Collaborate with content protection and security teams to ensure access controls and compliance.
• Champion engineering excellence by enforcing best practices in testing, code quality, and monitoring for ML pipeline components.
• Work with AI engineers and product managers to support use cases like scene segmentation, video annotation, and clip generation.

Required Skills

• 9-12 years of experience building scalable backend systems with deep cloud platform and ML system deployment experience.
• Proficiency in Java and Python.
• Experience with AWS and GCP.
• Solid understanding of Machine Learning and MLOps principles.
• Experience developing Microservices architectures.
• Familiarity with CI/CD practices.
• Proficiency in Go and JavaScript.

Preferred Skills

• Experience with Infrastructure-as-Code (IaC) principles.
• Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry).

Key Skills

Education

Any Graduate