To build and maintain robust ML pipelines and scalable deployment architectures for satellite, drone, LiDAR and temporal-based AI models, supporting data versioning, training workflows, and CI / CD for model delivery in a production environment
Develop and manage end-to-end ML pipelines for imagery-based AI solutions.
Handle preprocessing and versioning of geospatial data (satellite, drone, LiDAR).
Integrate models with APIs and deploy on cloud platforms (AWS / GCP).
Implement CI / CD and model monitoring for continuous delivery and performance tracking.
Work closely with AI and data teams to automate workflows using MLflow, Airflow, or Kubeflow.
Containerize solutions using Docker and manage deployments via Kubernetes.
Maintain experiment tracking, model registries, and reproducible ML environments.
Optimize infrastructure and data pipelines for large-scale image processing.
Education - B.E. / B.Tech in Computer Science, Data Engineering, or related field
Work Experience- Minimum 3+ years in ML / DevOps with real-world deployment of AI / ML models
Knowledge- MLOps principles, containerization, data pipelines, cloud infra
Skills- Python, MLflow, Docker, Kubernetes, Git, TensorFlow / PyTorch, FastAPI, AWS / GCP, Airflow / Kubeflow
Sr Developer • Kurnool, Andhra Pradesh, India