- Company Name
- Euphoric
- Job Title
- Lead AI Engineer
- Job Description
-
**Job title**: Lead AI Engineer
**Role Summary**
Design, build, and ship end‑to‑end AI systems that power an enterprise benefits platform. Balance AI architecture, production‑grade backend engineering, and data‑driven model optimization while mentoring a cross‑functional team and guiding product strategy.
**Expectations**
- Allocate ~40 % time to designing robust AI processing pipelines (LLMs, RAG, context engineering).
- Allocate ~40 % time to building APIs and services in Python (FastAPI, SQLAlchemy).
- Lead the transition from zero‑shot to data‑driven routing, instrumenting data collection, online evaluation, and simple ML models.
- Cross‑functional collaboration: UI/UX, frontend (React/TS) hacking, and product‑leadership advising.
- Continuous shipping mindset; deliver high‑quality features under time constraints.
**Key Responsibilities**
- Architect scalable AI pipelines integrating foundation models, chaining, and prompting strategies.
- Benchmark and validate AI outputs against business metrics and reliability tests.
- Develop and maintain backend services, data models, and APIs in Python on cloud platforms (GCP/AWS, Railway, Render, Vercel).
- Build and manage training data pipelines, perform bootstrapping, crowdsourcing, and cross‑validation.
- Deploy lightweight models (logistic regression, XGBoost, etc.) to optimize user outcomes via online evaluation.
- Fix production issues spanning backend, AI components, or frontend features.
- Mentor teammates on AI fundamentals, prompting best practices, and production ML principles.
- Provide strategic AI advice to product and leadership to prioritize high‑impact features.
**Required Skills**
- 5+ years production software engineering (Python, async frameworks, SQLAlchemy).
- Deep experience with foundation models/LLMs (context window, prompting, chaining).
- Proficiency in training/evaluating models (TensorFlow/PyTorch/Keras, scikit‑learn).
- Strong data engineering skills: dataset creation, validation, business‑metric evaluation.
- Familiarity with fast deployment on cloud (GCP, AWS) and modern PaaS (Railway, Render).
- Frontend fluency (React/TypeScript) for quick feature completion or debugging.
- Excellent communication, ability to translate complex AI concepts to non‑technical stakeholders.
- Pragmatic mindset for scalable, maintainable code and rapid iteration.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent practical experience).
- Advanced degrees or certifications in AI/ML are a plus but not mandatory.