Key Responsibilities :

- Design and implement MLOps pipelines for training, validation, deployment, and monitoring of machine learning models.

- Develop and maintain infrastructure for data versioning, model registries, and experiment tracking (e.g., MLflow, LakeFS, Airflow).

- Integrate orchestration tools (e.g., Kubeflow, Ray, Airflow) to support automated workflows and distributed training.

- Collaborate with data scientists and software engineers to ensure seamless model handoff and deployment.

- Build APIs and SDKs to abstract infrastructure complexity and enable self-service model development.

- Implement monitoring and alerting systems for model drift, performance degradation, and system health.

- Support on-prem and cloud-based deployments (e.g., Kubernetes, HPC clusters, AWS).

Qualifications :

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Software Engineering, or related field.

- 3+ years of experience in software development, preferably in AI/ML infrastructure or data platforms.

- Proficiency in Python and/or TypeScript/JavaScript.

- Experience with backend frameworks (e.g., FastAPI, Flask, Node.js) and frontend libraries (e.g., React, Vue).

- Familiarity with cloud services (AWS preferred), containerization (Docker), and orchestration (Kubernetes).

- Strong understanding of RESTful APIs, CI/CD pipelines, and Git-based workflows.

Preferred Qualifications :

- Experience with distributed training frameworks (e.g., Ray, Ray Tune)

- Knowledge of model explainability, monitoring, and rollback strategies.

- Exposure to hybrid cloud/on-prem infrastructure and HPC environments.

- Prior work on internal platforms or developer tools.