SDE 3

Responsibilities:
Design, build, and maintain backend services for deploying and serving ML models in production.
Develop high-performance APIs and services in Java, with supporting components in Python.
Deploy and operate services on AWS using ECS + Fargate, SageMaker, or EC2 with Kubernetes.
Enable both real-time (low-latency) and batch inference workflows.
Ensure service reliability, scalability, and performance in production environments.
Implement and maintain CI/CD pipelines and deployment automation.
Monitor systems using observability tools and proactively resolve production issues.
Collaborate with ML engineers to optimize model serving and integration workflows.
Requirements:
Strong backend engineering fundamentals with proficiency in Java.
Working knowledge of Python, especially in ML-related workflows.
Hands-on experience with AWS services (ECS, EC2 DynamoDB, Redis, S3 SageMaker).
Experience with containers and orchestration (Docker, ECS, Kubernetes).
Familiarity with Terraform or other infrastructure-as-code tools.
Experience building and operating production systems with CI/CD and monitoring.
Experience with observability tools (Datadog, New Relic, or similar).
Understanding of how ML models are deployed and served in production.
Strong problem-solving skills and ability to work in a fast-paced environment.

Experience
3-6 yrs