- Company Name
- Wine Labs
- Job Title
- AI Engineer
- Job Description
-
Job title: AI Engineer
Role Summary:
Design, build, and own the AI-driven data platform that transforms global wine market data into real‑time pricing and liquidity intelligence. Lead end‑to‑end LLM orchestration, extraction pipelines, and product features, working closely with founders and early users to iterate rapidly and deliver production‑ready solutions.
Expectations:
- Deliver high‑fidelity extraction and structured data at scale.
- Produce actionable insights and features powering a “Bloomberg Terminal” style wine market tool.
- Own the AI stack lifecycle, from model training to deployment, evaluation, and continuous improvement.
- Close feedback loops with early adopters, refining features based on real user impact.
- Demonstrate founder‑level ownership: focus on value‑generating work, not ticket backlog.
Key Responsibilities:
- Build LLM‑driven extraction pipelines that ingest merchant catalogs, auction listings, and other unstructured sources, converting them into strict schemas.
- Develop Retrieval‑Augmented Generation (RAG) workflows enabling natural language queries over structured wine data.
- Design, train, and deploy generative models to synthesize critic reviews, explain price volatility, and predict liquidity trends.
- Manage model evaluation metrics, monitor performance, and iterate on models to improve accuracy and relevance.
- Oversee infrastructure for data ingestion, storage, and model deployment (cloud or on‑prem).
- Collaborate with the CEO on AI strategy and technical architecture decisions.
- Engage with early users to validate features, troubleshoot friction points, and guide product roadmap.
Required Skills:
- Strong expertise in LLM orchestration, agent design, and multi‑step task automation.
- Proficiency with NLP, text extraction, and structure conversion technologies.
- Experience building and deploying RAG systems and generative AI applications.
- Solid software engineering background: clean code, version control, CI/CD, and scalable architecture.
- Familiarity with data pipelines, cloud services, and monitoring tools.
- Ability to evaluate models quantitatively (precision, recall, MSE, BLEU, etc.) and qualitatively (user impact).
- Proven ownership of end‑to‑end projects; comfortable driving from concept to production.
- Knowledge of financial market concepts such as bid/ask spread, liquidity, and order books, with ability to apply them to commodity data.
- Curiosity and analytical mindset to interpret complex pricing signals in physical asset markets.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering, or related field, or equivalent combination of education and experience.
- Certifications in relevant areas (e.g., cloud platform, ML Ops) are a plus.