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Senior Full Stack Java Engineer

Job in Centennial, Arapahoe County, Colorado, USA
Listing for: Yield-Solutions-Group-LLC-
Full Time position
Listed on 2026-07-15
Job specializations:
  • Software Development
    Full Stack Developer, Backend Developer, Software Architect, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 150000 - 170000 USD Yearly USD 150000.00 170000.00 YEAR
Job Description & How to Apply Below

Salary Range: $ To $ Annually

Location:

Centennial, CO ( In-Office – No relocation) Company:
Yield Solutions Group

Reports to:

Director of Software Development

The Opportunity

Yield Solutions Group processes more than thousands of auto refinance applications per month across 30+ lending partners. The systems that support that volume handle real financial data, operate under regulatory constraints, and need to perform reliably under load.

This is a senior individual contributor role with a front‑end emphasis and full architectural ownership. Where the Full Stack Java Engineer contributes to architectural decisions, you make and are accountable for them. You design the systems other engineers build within. You set the standards other engineers are held to in code review. You are the escalation point when a technical challenge requires judgment beyond what the team can resolve independently.

We treat AI‑assisted development as a core engineering discipline, not a productivity shortcut. Our approach is grounded in Nate B Jones' five levels of AI coding: engineers here are expected to work at the spec‑driven and agentic levels of that framework. At the senior level, that means not only practicing these disciplines yourself but actively raising the team's fluency in them.

You are the practitioner other engineers look to when they need to know how to structure a spec, evaluate agent output, or apply the delegation model to a problem they have not encountered before.

You will not have direct reports. Your leverage is your technical judgment, the standards you set in review, and the clarity of your architectural decisions. You will not be handed fully‑specced tickets. You are expected to shape requirements before implementation begins, identify architectural problems before a line of code is written, and resolve technical blockers without escalation.

What You’ll Do
  • Own architectural decisions for complex, cross‑service features. This is not influence from the side; it is accountability for the outcome. When an architectural decision goes wrong, it is yours to fix.
  • Set and enforce front‑end and full‑stack standards across the team:
    React component architecture, Type Script patterns, state management conventions, API contract design, test coverage thresholds, and AI engineering practice.
  • Conduct authoritative code reviews that evaluate architectural fit, service boundary integrity, performance under load, and security posture alongside correctness. This applies with equal rigor to AI‑generated code, which carries its own failure modes: hallucinated APIs, plausible‑but‑wrong logic, and implementations that pass tests but violate service boundaries or data contracts.
  • Apply and model the delegation model from Nate B Jones' five levels framework: make principled, documented decisions about which tasks to delegate to AI agents, at what level of autonomy, and where human ownership is non‑negotiable. Other engineers look to you to calibrate their own judgment against yours.
  • Raise architectural concerns during sprint planning, design reviews, and roadmap discussions before they become production problems. At this level, that means shaping the conversation, not reacting to it.
  • Mentor Full Stack Java Engineers and earlier‑career engineers on both technical fundamentals and AI engineering practice: spec authoring, agent output validation, delegation model application, and the discipline of treating AI instructions as engineering artifacts.
Discovery & Execution
  • Author structured spec files that define problem scope, constraints, acceptance criteria, and edge cases before any implementation begins, whether by a human or an AI agent. The spec is the source of truth. AI output is an implementation candidate that must be validated against it.
  • Craft prompts and AI agent instructions with engineering precision: decompose problems clearly, specify constraints explicitly, and iterate based on what the output reveals about gaps in the original intent.
  • Design REST APIs and inter‑service contracts across the microservices architecture that are stable, versioned, and defensible. You are setting contracts that other services depend on; treat…
Position Requirements
10+ Years work experience
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