AI Product Engineering
Scope & Responsibilities
- Prototype LLM-powered tools, copilots, and retrieval workflows using APIs, embeddings, and orchestration frameworks
- Build full-stack AI features: retrieval logic, prompt pipelines, caching, evaluation, observability
- Own the full AI lifecycle—from data prep and experimentation to deployment and monitoring
- Evaluate new models (OpenAI, Claude, Gemini, open-source) and quickly test what works
- Improve system performance across accuracy, latency, hallucination, and user experience
- Translate ambiguous needs into scoped AI features with clear goals and success metrics
- Prioritize experiments and features based on impact, feasibility, and data
Experience Required
- 5+ years software engineering or ML experience, including 3+ years with LLMs, NLP, or applied AI
- Proven track record shipping production AI tools or platforms
- Strong with Python, LangChain/OpenAI SDK, embeddings, RAG, and vector DBs
- Experience optimizing LLM workflows (latency, cost, reliability)
- Ability to think from first principles and operate across technical + product domains
- Experience with fine-tuning, LoRA, or open-source model hosting (HF, Ollama, BentoML)
- Familiarity with agentic design (LangGraph, task decomposition frameworks)
- Exposure to product analytics and A/B testing
- Experience building internal tools or automation that improved efficiency
- Ability to design user flows while optimizing prompts or embedding indexes
Personal Attributes
Someone who thrives on turning the latest LLM and GenAI advances into real, working products—part architect, part engineer, part product hacker. Comfortable taking ideas from concept to prototype to production quickly.
Education
Bachelor’s degree required; Master’s in CS or Data Science preferred.
