Design & Build: Move beyond LLM prompts. You will architect end-to-end intelligent systems using the right tool for the job—whether that’s a Gradient Boosted Tree, a Transformer, or a custom CNN.
Own the Pipeline: Build and maintain the "plumbing" (Airflow, dbt) and the "orchestration" (MLflow, ZenML) of our AI products.
Research & Experiment: Apply a research-oriented mindset to solve complex problems like multi-hop reasoning, model quantization for edge devices, and advanced retrieval.
Evaluate & Refine: Don't just ship it—verify it. You'll build frameworks to detect hallucinations, measure regression, and ensure our models are safe and explainable.

Strong ML Fundamentals: Proven experience solving problems with traditional ML (XGBoost, LightGBM) and deep feature engineering.
Deep Learning Expertise: Hands-on experience with CNNs, RNNs, or Transformers.
Research Background: A strong plus. You can read a paper and implement the findings.
The Full-Stack Mindset: Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-Learn), plus experience with the modern data stack (SQL, dbt, Airflow).

Full-Stack AI Engineer

Key skills