Senior ML/AI Engineer_Hybrid (NYC)
Hybrid · New York City, New York, United States
Job Summary
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 robust ML pipelines (data preprocessing, feature engineering, model training, evaluation, production deployment); architect and improve the production graph RAG system; build RAG systems and LLM integrations powering natural language interfaces and autonomous workflows; collaborate with backend engineers to ensure production-grade models optimized for latency, reliability, and scale; own model performance end-to-end through monitoring, retraining, and continuous improvement; stay at AI frontier and bring relevant innovations into the platform.
Required Qualifications
- 5+ years of experience in applied machine learning and AI
- MS or PhD in Computer Science, Machine Learning, Statistics, or a related field — or equivalent practical experience
- Python proficiency with 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
Desired Qualifications
- 5+ years of experience in applied machine learning and AI
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field — or equivalent practical experience
- Deep proficiency in Python with 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; 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
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