At The ReWork Group, we partner with high-growth startups and forward-thinking companies to build the future.
Our client is building a platform where AI doesn't just analyze enterprise data; it reasons across it, forecasts what's coming, and acts on its own.
Demand forecasting.
Consumer intelligence.
Competitive analysis.
Autonomous decision-making.
As a Senior AI Engineer, the models you build won't sit in a notebook waiting for a publication cycle. They'll run in production, at enterprise scale, making real calls on messy, heterogeneous, real-world data.
This is applied AI at its most impactful. They run experiments at the fringes of what's possible — machine learning, graph databases, agentic architectures — and they want engineers who are restless about staying at the frontier and obsessive about turning that edge into product value.
It's a hands-on role, end to end: prototype to production, and keeping it alive at scale.
If "agentic AI" and "graph RAG" in the same sentence make you lean in, keep reading.
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 our agentic AI architecture — systems that reason across heterogeneous data sources and take autonomous action.
- Own robust ML pipelines end to end — data preprocessing, feature engineering, model training, evaluation, and production deployment.
- Architect and sharpen our production graph RAG system — one of their core technical differentiators.
- Build RAG systems and LLM integrations that power natural-language interfaces and autonomous workflows.
- Partner with backend engineers to make models genuinely production-grade — tuned for latency, reliability, and scale.
- Own model performance in the wild — monitoring, retraining, and continuous improvement once it's live.
- Stay at the frontier of AI research and pull the innovations that actually matter into the platform.
Who You Are:
- Senior enough to think deeply about architecture and tradeoffs — but you still have boundless energy for implementation. You'd rather build it than describe it.
- High agency, low ego. You move without waiting for permission, and you don't need the credit.
- A great communicator who can make complex systems legible to the people who depend on them.
- 5+ years of experience in applied machine learning and AI, with models deployed and running in production environments
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or 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)
- Experience with NLP, LLMs, and RAG architectures.
