The ideal candidate will be responsible for working cross-functionally to understand data and AI needs across multiple business units. To be effective in this position, you must feel comfortable owning the entire data science workflow, including leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques, from data collection and preprocessing to model development, evaluation, and deployment.
[Job Description]
- Senior Data Scientist is responsible for developing and delivering elements of engineering solutions to accomplish business goals.
- Develop and optimize RAG pipelines to enhance LLM-driven troubleshooting.
- Work with PyVector to store and retrieve relevant data efficiently.
- Implement vector embedding models to power retrieval and improve accuracy.
- Build high-quality Python code to power our AI applications.
- Deploy and scale models on Azure.
- Fine-tune and evaluate LLMs for task-specific reasoning and accuracy.
- Implement efficient document indexing and retrieval for knowledge-intensive queries.
- Experiment, iterate, and deploy features quickly based on real customer feedback.
- Work with a small but highly skilled team to push the limits of industrial AI.
- Take ownership of challenges, figure things out as you go, and adapt to changing priorities.
[Requirements]
- B.S. with 5 years of industry experience OR M.S. with 3 years of industry experience, OR PhD in Computer Science, Math, Statistics, a related field with a focus on NLP/LLM/RAG.
- Strong Python development skills, with experience in machine learning or NLP.
- Experience with RAG architecture using LangChain, Llamaindex and knowledge retrieval techniques.
- Familiarity with PyVector for vector search and retrieval.
- Experience using vector embedding models (e.g., OpenAI, Bedrock Titan, Cohere, SentenceTransformers).
- Understanding of embedding models and best practices for retrieval.
- Experience working with Azure for cloud-based ML deployment.
- Knowledge of LangChain, LlamaIndex, or other RAG frameworks.
- Ability to work in a fast-paced, experimental environment, where iteration and adaptation are key.
- A self-starter who can work autonomously in a remote setting.
- A strong problem-solver who thrives when thrown in at the deep end.
