Senior ML/AI Engineer_Hybrid (NYC) job in New York

The platform connects an organization's entire data landscape - internal systems, social media trends, industry reports, consumer behavior signals - into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks. At its core is a production graph RAG system connecting temporal and sentiment data at enterprise scale - a key technical differentiator. You will work at the intersection of applied ML, agentic AI, and graph-based reasoning. The company runs experiments at the fringes of modern technology - ML, graph databases, agentic AI - and wants engineers who share the drive to stay at the frontier and turn innovation into real product value. This role spans prototype to production, and everything in between., 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 the agentic AI architecture - building systems that reason across heterogeneous data sources and take autonomous action

Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment

Architect and improve the production graph RAG system

Build RAG systems and LLM integrations that power natural language interfaces and autonomous workflows

Collaborate with backend engineers to ensure models are production-grade - optimized for latency, reliability, and scale

Own model performance end-to-end: monitoring, retraining, and continuous improvement in production

5+ years of experience in applied machine learning and AI, with models deployed and running in production

M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field - or equivalent practical experience (what you've built matters more than the degree)

Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)

Strong background in statistical analysis, predictive modeling, and time series forecasting

Experience with applied agentic AI/ML systems and multi-agent orchestration

Experience with NLP, LLMs, and RAG architectures

Comfort working with large-scale datasets and distributed computing environments

Nice to have

Graph database or graph RAG experience (a major plus - core to the stack)

Background in retail, supply chain, or demand forecasting domains

Experience with graph neural networks or knowledge graphs

Familiarity with MLOps platforms and model serving infrastructure

Contributions to open-source ML/AI projects or published research