About The Role

ABOUT THE ROLE

This is a senior individual contributor role on the core AI team of an early-stage, AI-native consumer intelligence platform. You'll design and deploy the intelligent systems — from demand forecasting models to agentic reasoning architectures — that deliver real, measurable value for large enterprise retail clients. The work sits squarely at the intersection of applied research and production-grade deployment.

WHAT YOU'LL DO

Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale.
Develop and iterate on an agentic AI architecture that reasons across heterogeneous data sources and takes autonomous action.
Build and maintain end-to-end ML pipelines covering data preprocessing, feature engineering, model training, evaluation, and production deployment.
Architect RAG systems and LLM integrations that power natural language interfaces and autonomous workflows.
Collaborate with backend engineers to ensure models meet production standards for latency, reliability, and scale.
Own model performance end-to-end: monitoring, retraining, and continuous improvement in production.
Stay current with AI research and bring relevant innovations into the platform.

WHAT WE'RE LOOKING FOR

3+ years of hands-on ML experience, focused on building and delivering production ML systems (not primarily software engineering with minimal ML exposure).
Deep proficiency in Python and at least one major ML framework (PyTorch, TensorFlow, or equivalent).
Production experience building agentic systems and LLM harnesses (e.g. using LangChain or similar).
Hands-on experience with graph databases.
Strong SQL skills for data querying and manipulation.
M.S. or Ph.D. in Computer Science, Machine Learning, or a closely related field.
Background working at scale — large tech companies or high-growth startups with live, production ML systems.
Go programming language experience is a plus.