Job Description
Own end-to-end ML/AI projects: problem framing, data pipelines, modeling, offline/online evals, deployment, monitoring, and iteration.
• Build and productionize agentic workflows (tool-using/multi-step agents with retrieval, planning, and human-in-the-loop), including safety/guardrails and reliability.
• Train classical ML models (tree ensembles, linear models, anomaly detectors, time-series forecasting) and deep learning models when appropriate.
• Operationalize models with CI/CD, feature stores, reproducible training, and model registries
• Monitor and improve live systems: data & concept drift detection, performance regression, bias/fairness, cost/latency; drive remediation playbooks.
• Partner cross-functionally with product, data, and platform teams; write clear design docs
• Work as an Individual contributor with minimal directions
• Should be able to interact with stakeholders, understand the problem statement, and come up with solutions