AI Engineer – Agentic Systems
Job in
Fairfield, Fairfield County, Connecticut, 06828, USA
Listed on 2026-02-16
Listing for:
Sacred Heart University
Full Time
position Listed on 2026-02-16
Job specializations:
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below
and the job listing Expires on February 15, 2026
AI Engineer — LLMs, GenAI & Agentic Systems About BON CREDITBON 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.
- 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
- 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:
- Embeddings, similarity search, ranking
- Where LLMs fail — and how to design around it
- Experience deploying LLM systems in production:
- Cost, latency, and reliability trade‑offs
- Proven work with:
- Fine‑tuning (and knowing when not to fine‑tune)
- 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
- 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
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