Responsibilities

• Architect, build, and maintain end-to-end AI/ML pipelines (data ingestion, preprocessing, training, deployment, predictive analytics / monitoring).
• Collaborate with data scientists and domain experts to operationalize experimental models, optimizing for performance, scalability, and latency to ensure high-quality datasets.
• Implement and advocate for AI/MLOps practices (CI/CD for ML, model versioning, feature stores) using modern tools.
• Optimize model inference for production environments (e.g., using TensorRT, ONNX, pruning, quantization).
• Write robust, testable, and maintainable code in a collaborative setting using GitHub.
• Integrate AI models into enterprise systems using APIs and cloud-native services (AWS, Azure).
• Ensure models meet business objectives while adhering to ethical AI and governance frameworks.
• Provide technical support and training to users.
• Troubleshoot and re...