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AI Architect

Fynbosys IncAustin, TX🇺🇸United StatesPosted 22 Jul 2026

Why This Role Stands Out

As an AI Architect at Fynbosys Inc, you'll own the end-to-end architecture of critical AI systems, driving innovation and seeing your designs come to life. This hands-on role is perfect for experienced engineers with a passion for building scalable AI solutions who thrive in a collaborative, hybrid environment. Apply today to make a significant impact in the evolving AI landscape.

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Hi
Role: AI Architect 
Location:  Austin, Charlotte, San Diego, NYC

Experience: 15+ years engineering experience, including significant demonstrated impact designing and
deploying production AI systems at scale.
The hands-on technical lead and the one broad role on the pod. You own the Detection &
Disposition Engine architecture end to end, you are the senior voice in the joint working sessions,
and you write code and review pull requests. Your first job is to convert an open-ended "research
task" into a typed, measurable decision pipeline.

Responsibilities
  • Own the end-to-end architecture of the Disposition Engine: the item ledger, deterministic
  • evidence-pack assembly, the rule ladder, the AI reasoning tier, the validation harness, and the
  • closed feedback loop.
  • Scale the end-to-end architecture of the existing Detection Engine.
  • Decide, per problem, what is deterministic and what genuinely requires reasoning — and defend
  • that boundary. This judgment is the core of the role.
  • Design the cross-workstream coupling: how a single schema disposition cascades into batch
  • jobs and code findings, and how coupled items move together as change waves.
  • Lead design working sessions; turn ambiguity into a structured decision space and a burn-down
  • the whole team can watch daily.
  • Own the gold-set evaluation strategy and the metrics that prove leverage — auto-disposition
  • rate, accuracy against decisions already made by hand, human-minutes per item, and cost per
  • item.
  • Drive design-to-code: write production code, review PRs, and oversee testing across the pod.
  • Mentor the pod and champion a culture of rigor, velocity and auditable AI.
Qualifications
  •  Python — production-grade.
  • Agentic / multi-agent architecture; orchestration (LangGraph or equivalent), typed state, tool
  • contracts; context and prompt engineering; evaluation engineering.
  • Demonstrable judgment on deterministic versus probabilistic system design — you can point to
  • systems where you deliberately kept the model out of the critical path.
  •  RAG and code/knowledge-graph design; retrieval-pipeline design and tuning.
  •  Production LLM systems at scale on AWS (Bedrock) or equivalent.
  •  Evaluation harnesses: golden sets, confidence calibration, regression suites that gate model and
  • prompt changes.
  •  Spec-driven development; power user of AI coding agents (Claude Code, Cursor, Codex).
  •  Outstanding written and verbal articulation.
  • Working knowledge
  • Large-scale legacy migration or code-remediation programmes (database, batch or application)
  • — a strong plus.
  • SQL Server and relational migration; Informatica; graph databases (Neo4j / Neptune).
  • CI/CD automation; prior delivery in a regulated financial-services environment.

Skills

Neo4j
SQL
SQL Server
AWS
Assembly
LLM
Python

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