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