Own the end-to-end machine learning lifecycle, including data curation, model training, optimization, packaging, deployment, monitoring, and production releases.

Design and implement automated model validation frameworks, regression testing, slice-based evaluation, and release scorecards to ensure model quality across multiple deployment environments.

Build scalable MLOps pipelines using Docker, Jenkins, Kubernetes, MLflow, and Airflow to automate model training, validation, and deployment.

Work across AWS and GCP cloud environments to manage ML workloads, datasets, model training, and deployment pipelines.

Automate validation, reporting, and analytics workflows to reduce manual effort and improve release efficiency.

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electronics, or a related engineering discipline.

2–5 years of experience developing and deploying production machine learning solutions.

Strong knowledge of Computer Vision, Object Detection, and Deep Learning frameworks (PyTorch, TensorFlow, or similar).

Strong Python programming skills with experience in API development (Flask/FastAPI).

Experience building CI/CD pipelines using Docker and Jenkins.

Experience with Kubernetes, Airflow, and MLflow.

Hands-on experience with AWS services (EC2, SageMaker, S3) and GCP services (Vertex AI, BigQuery, Cloud Storage).