Description

Key Responsibilities

• Design and implement scalable cloud architectures and ML pipelines using AWS and container technologies.
• Build reliable, cost-efficient solutions with Python, SageMaker, Docker, ECS/Fargate and related services.
• Lead technical design reviews, produce high-level and low-level designs, and guide engineering teams on best practices.
• Develop and maintain APIs and microservices; ensure strong system integration and test coverage.
• Drive quality and operational excellence through testing, performance tuning, monitoring, and root-cause analysis.
• Participate in on-call rotation and provide production support when needed.
• Mentor junior engineers and contribute to continuous improvement of processes and tooling.

Qualifications

• Bachelor's degree in Computer Science or related field; master's degree preferred.
• 8+ years of engineering experience with several years in technical lead or architect roles.
• 5+ years designing and deploying solutions on AWS using services such as SageMaker, Lambda, Step Functions, S3, ECR, EKS, ECS/Fargate, RDS/Aurora.
• Strong hands-on experience with Python and frameworks such as Flask, Django, and AsyncIO.
• Proven background in MLOps platforms (SageMaker, Kubeflow, MLflow) and workflow orchestration (Airflow).
• Solid experience with CI/CD pipelines and tools (CodeCommit/CodeDeploy/CodePipeline, Jenkins, Harness).
• Experience with distributed systems, system integration, async messaging (Kafka, RabbitMQ, SQS), logging/monitoring strategies, and production troubleshooting.
• Excellent problem-solving, communication, and documentation skills; comfortable working in agile/scrum teams and mentoring others

Key Skills

Education

Bachelor's degree