Description

Build large-scale, cloud-based big data and MLOps platforms and APIs to access timely, accurate, and relevant data.

This role is hybrid.

Responsibilities

• Partner with Architecture, Product, and Engineering teams to craft scalable, flexible, and resilient cloud architectures.
• Design and implement complex architectural solutions adhering to AWS design principles and industry standards.
• Build scalable, reliable ML pipelines using Python and AWS services such as SageMaker, Lambda, Step Functions, S3, and ECR.
• Lead technical design reviews, guide engineering teams, and create high-level and low-level design documents.
• Drive continual improvement in quality and efficiency, including defect prevention and root cause analysis.

Required Skills

• 8+ years of experience in key engineering roles (technical lead, software engineer, or software architect).
• 5+ years of experience architecting and deploying cloud-native solutions on Amazon Web Services (AWS).
• Hands-on design and development experience using Python, Flask, and Django.
• Strong experience with MLOps platforms, including AWS SageMaker, Kubeflow, or MLflow.
• Deep understanding of distributed systems, integration, testing, and troubleshooting.
• Demonstrated familiarity with CI/CD processes and tools (e.g., CodePipeline, Jenkins, Harness).
• Experience designing and developing APIs, Real-Time Systems, and Microservices.
• Familiarity with AWS services including ECS, S3, Lambda, and Aurora.
• Experience integrating with async messaging/logging/queues such as Kafka or SQS.

Preferred Skills

• Experience with Kubernetes and container orchestration beyond basic ECS.
• Background in data engineering or large-scale data processing systems.

Key Skills

Education

Any Graduate