AI Engineer - LLMs, GenAI & Agentic Systems
BON Credit (Bhim Digital, Inc.)
📍 Palo Alto, California | ⏳ Full-time | 🚀 Start: Immediate
About BON Credit
BON CREDIT is building an AI-first consumer fintech that helps Americans get out of credit card debt- intelligently and consistently.
We’re not shipping another finance dashboard.
We’re building an always-on AI financial companion that understands users, builds plans, tracks progress, and nudges behavior - every single day.
This role isn’t about “supporting AI.”
You’ll be shaping how the product thinks, speaks, and acts.
What you’ll do
You’ll own the AI layer end-to-end and turn cutting-edge GenAI into a real product people trust.
- Build and scale BON CREDIT’s agentic AI systems
- Design workflows for financial planning, progress tracking, and motivation
- Create LLM-powered conversational experiences that feel calm, human, and trustworthy
- Build production-grade RAG pipelines using user data, financial logic, and rules
- Integrate LLM APIs with strong observability, safety, and reliability
- Partner closely with product & design to translate UX into AI behavior
- Continuously improve response quality, structure, and usefulness
- Ensure outputs are safe, compliant, and fintech-ready
What we’re looking for: Core skills
You don’t just use GenAI - you understand how it really works.
- Strong experience with Generative AI, LLMs, and ML fundamentals
- Hands-on building real user-facing AI products (not just demos)
- Deep understanding of:
- Prompt design, system prompts, tool calling, context management
- Embeddings, similarity search, ranking
- Where LLMs fail - and how to design around it
- Experience deploying LLM systems in production:
- Logging, monitoring, evaluation loops
- Cost, latency, and reliability trade-offs
- Proven work with:
- RAG pipelines
- Fine-tuning (and knowing when not to fine-tune)
- Feedback loops & human-in-the-loop systems
LLM systems & conversational AI
- Integrating LLM APIs into backend systems
- Tool/function calling and agent orchestration
- Designing multi-turn conversations that stay coherent
- Managing memory, context decay, and user state
- Building assistants that guide, not overwhelm
Bonus points if you…
- Explain complex AI behavior clearly to non-technical teammates
- Think in product outcomes, not just technical elegance
- Learn fast in ambiguity and love shipping
- Take feedback like a pro and iterate even faster
Hiring process
- Quick intro chat
- Deep dive into AI systems you’ve built
- Technical interview
- Final product & culture conversation
