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AI Engineer – Agentic Systems

Job in Fairfield, Fairfield County, Connecticut, 06828, USA
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
Salary/Wage Range or Industry Benchmark: 100000 - 130000 USD Yearly USD 100000.00 130000.00 YEAR
Job Description & How to Apply Below

and the job listing Expires on February 15, 2026

AI Engineer — LLMs, GenAI & Agentic Systems 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
  • 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:
  • 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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