Responsibilities
- Modeling & Analytics: Explore data, engineer features, develop and evaluate ML/AI models (supervised/unsupervised, NLP, generative/LLM use cases), and communicate insights clearly to stakeholders.
- LLM & RAG Solutions: Build and optimize LLM‑based applications (prompting, fine‑tuning/adapter methods) and implement RAG pipelines for grounded responses and enterprise use.
- Applications & Tooling: Develop end‑to‑end apps using Streamlit (frontend + backend) and build scalable workflows and Databricks Apps for data processing, experimentation, and deployment.
- Data & MLOps: Work with Python/SQL and modern data platforms to productionize models (versioning, monitoring, retraining, CI/CD) with an emphasis on performance, reliability, and security.
- Collaboration: Partner with product, engineering, and business teams to identify use cases, scope experiments, and translate outcomes into measurable impact.
Qualifications
Required
- Bachelor's degree in a quantitative field (e.g., Data Science, Computer Science, Statistics) or equivalent practical experience.
- Proficiency in Python and SQL;
experience with ML libraries/frameworks (e.g., scikit‑learn, PyTorch or TensorFlow, Hugging Face).
- Hands‑on experience with LLMs, RAG architectures, and evaluation methods.
- Practical experience building Databricks workflows or Databricks Apps.
- Ability to create Streamlit apps end‑to‑end (UI, business logic, and model integration).
- Strong problem‑solving skills and the ability to communicate technical concepts to non‑technical audiences.
Preferred
- Experience with vector databases and embeddings; familiarity with data lakes/Delta formats and distributed computing (e.g., Spark).
- Knowledge of MLOps practices (MLflow, CI/CD, monitoring) and containerization (Docker).
- Exposure to at least one major cloud platform (Azure/AWS/GCP).
- Understanding of data governance, privacy, and responsible AI principles.