Roles & Responsibilities
- Design and build reproducible machine learning pipelines for training, evaluation, and deployment.
- Implement CI/CD pipelines for ML models using cloud-native services like Cloud Build for GCP, CodePipeline for AWS, and Azure DevOps for Azure.
- Deploy and manage models using cloud-native services such as Vertex AI, SageMaker, or Azure ML, with proper security controls.
- Set up automated monitoring to detect model drift, performance degradation, and data quality issues.
- Maintain model registries and track versioning and lineage across development and production environments.
- Ensure model reliability, scalability, and performance in production environments.
- Help transition proof-of-concept projects into robust, production-grade systems.
- Establish a robust cloud ML infrastructure to reduce model deployment time.
Requirements
- Bachelor's degree or above in Computer Science, Software Engineering, or a related field.
- Minimum of 3 years of experience in MLOps development.
- Strong Python programming skills and proficiency with DevOps practices and infrastructure-as-code (Terraform).
- Hands-on experience with managed ML platforms like Vertex AI, SageMaker, or Azure ML is essential.
- Proficiency in containerization (Docker) and orchestration (Kubernetes).
- Experience with CI/CD tools and monitoring stacks (Prometheus, Grafana, Cloud Monitoring).
- Strong problem-solving and analytical thinking abilities.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
We regret that only shortlisted candidates will be notified. Thank you.
