[Hiring] AI Application Engineer @RouteGenie

Role Description

You'll work with and bring opinions on choosing between the following:

• LLM Providers & APIs: Anthropic Claude (primary), OpenAI, AWS Bedrock
• Local / Self-Hosted LLMs: Ollama, LM Studio, llama.cpp, vLLM; open-weight model families (Llama, Qwen, Mistral, etc.)
• Agent Frameworks: LangChain / LangGraph, LlamaIndex, OpenAI Agents SDK, or equivalent
• Retrieval & Knowledge: Vector databases (Pinecone, Weaviate, pgvector); RAG, cache-augmented generation, tool-based agentic retrieval, GraphRAG, hybrid approaches
• Voice AI: ElevenLabs, VAPI, LiveKit, Deepgram
• LLM Observability & Eval: LangSmith, Braintrust, Phoenix, Helicone, or similar
• AI-Assisted Development: Claude Code
• RouteGenie Stack: Python, Django, PostgreSQL, Angular, TypeScript

Qualifications

• Experience: 3+ years of software engineering experience.
• Production AI: 1+ year hands-on experience with production LLM / AI features shipped to real users (not prototypes or coursework).
• Languages: Strong Python skills; comfort with TypeScript.
• Frameworks: Hands-on experience with at least one agent framework and multiple retrieval/context-augmentation approaches.
• APIs: Production experience with major LLM provider APIs from our Tech Stack.
• Architectural Judgment: Sound judgment on AI architecture choices. Ability to select the right model and execution environment against cost, latency, accuracy, and data-residency constraints.
• Quality Measurement: Demonstrated experience measuring AI feature quality in production.
• Communication: Working professional English; strong async written communication for collaboration across Mexico, Europe, and US time zones.

Requirements

• Voice AI: Experience with Voice AI. NEMT dispatch and customer-service flows are voice-heavy, and voice agents will be a major product surface.
• Regulated Data: Experience in a healthcare or regulated-data context (HIPAA, PII/PHI handling).
• Self-Hosting: Local / self-hosted LLM experience running open-weight models on-prem or in a VPC.
• Anthropic Ecosystem: Claude API / Anthropic SDK experience — including Claude-specific patterns.

Preferred (Nice-to-Have)

• LLM observability / eval tooling experience (LangSmith, Braintrust, Phoenix, Helicone, or similar).
• Cost and latency optimization at LLM scale (prompt caching, model routing, token budgeting).
• Traditional ML / data science background (model training, feature engineering, evaluation methodology).
• Django / PostgreSQL background.
• Multi-tenant SaaS experience.
• Open-source AI contributions or public agent projects.