Key Responsibilities :

- Design, develop, and maintain scalable ML platforms and production-grade ML pipelines.

- Build and operationalize machine learning workflows using AWS SageMaker and MLOps practices.

- Develop robust and scalable applications and services using Python.

- Design and implement microservices and APIs for ML and data-driven applications.

- Containerize applications using Docker and implement reliable CI/CD pipelines.

- Work closely with data scientists, ML engineers, and software engineering teams to productionize ML models.

- Drive system architecture, technical design, and engineering best practices.

- Troubleshoot performance, scalability, reliability, and deployment issues in production environments.

- Provide technical leadership and contribute to architectural and technology decisions.

- Ensure solutions are scalable, secure, maintainable, and aligned with business requirements.

Required Skills & Experience :

- 7+ years of overall software/ML engineering experience.

- 5+ years of hands-on AWS experience.

- Strong programming expertise in Python.

- Strong experience with MLOps, AWS SageMaker, and ML pipelines.

- Experience designing and developing microservices and APIs.

- Hands-on experience with Docker/containers and CI/CD.

- Strong understanding of system design, architecture, scalability, and production engineering.

- Demonstrated technical leadership and technical design experience.

Good to Have :

- Flask/Django

- AsyncIO

- Kubeflow

- MLflow

- Amazon Aurora

- RabbitMQ

- Harness

- AWS CodeCommit

Preferred Candidate Profile :

- Strong problem-solving and analytical skills.

- Experience building and operating production-grade ML systems.

- Ability to work effectively across ML, software engineering, and platform teams.

- Strong communication skills with the ability to influence technical decisions and mentor engineering teams.