Description:
This is a hands-on role for someone who has built and shipped LLM-powered applications, not just trained models in a notebook.
What you'll be doing:
- Developing, fine-tuning, and deploying ML models that power live AI products
- Working with LLMs and Retrieval-Augmented Generation (RAG) on real business problems
- Building and maintaining robust data preprocessing pipelines
- Monitoring and continuously improving models against real-world performance
- Partnering with stakeholders to turn business challenges into ML solutions
What you'll bring:
- Master's or PhD in AI, Data Science, Computer Science, Statistics or similar
- 5+ years in ML, Data Science, RL or a related field
- Hands-on experience with LLMs and parameter-efficient fine-tuning (LoRA, QLoRA)
- Strong Python. Competitive coding background a plus (Kaggle, ACM/ICPC, TopCoder)
- Experience deploying LLM apps with FastAPI/Flask, plus Streamlit for front ends
- Solid grasp of REST, HTTP methods, websockets, API lifecycle
- ML frameworks (PyTorch, TensorFlow, scikit-learn) and LangChain, LangGraph, LlamaIndex
- SQL/NoSQL (PostgreSQL, MongoDB, Redis) and vector databases (Pinecone, Weaviate, pgvector)