What’s in it for you as an employee of QFG?
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Health & wellbeing resources and programs
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Paid vacation, personal, and sick days for work-life balance
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Competitive compensation and benefits packages
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Career growth and development opportunities
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Opportunities to contribute to community causes
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Work with diverse team members in an inclusive and collaborative environment
We’re looking for our next Senior Principal Software Engineer. Could It Be You?
We are looking for a Senior Principal Software Engineer to operate as the highest-level individual contributor in our engineering organization.
The most important pillar for this role is: leading our transformation into an AI-native engineering culture, where coding agents, AI workflows, and continuous learning are core to how every engineer works.
You will own the most complex technical decisions in your domain, set the bar for how AI is built into our products and used inside our team, and coach engineers at all levels to dramatically increase their leverage. You will be the person other principals come to when an architecture problem is too big or too ambiguous, and the person every engineer learns from when they ask, "how do I actually build with AI well?"
Need more details? Keep reading…
Lead AI-Augmented Engineering and Velocity
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Set the strategy for how the engineering organization uses AI coding agents (Claude Code, Cursor, GitHub Copilot, Codex, and successors) across the full SDLC: planning, scoping, design, implementation, code review, debugging, testing, documentation, and operations.
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Design AI-augmented workflows that connect coding agents to issue trackers, design systems, CI/CD, and observability — turning specs into scoped subtasks, generating boilerplate that matches conventions, and automating routine maintenance.
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Establish review patterns and quality gates for AI-generated code so the team ships faster without sacrificing security, correctness, or maintainability. Be the last line of judgment on what AI produces.
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Stay current on the frontier of coding agents, agentic frameworks, and developer tooling — and translate that knowledge into team practices within weeks, not quarters.
Nice to Have
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Lead the architecture and delivery of LLM-powered features and agentic systems that run in production: orchestration, tool use, multi-agent coordination, retrieval-augmented generation, and structured outputs.
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Define context engineering and prompt architecture standards: system prompts, instruction hierarchies, persona definitions, and the high-level behavioral constraints that frame agentic pipelines.
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Own the evaluation strategy for AI products end-to-end: offline eval sets, automated harnesses for relevance/groundedness/safety, regression tests across model versions, and online metrics that connect model behavior to user outcomes.
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Design RAG and retrieval pipelines responsibly: chunking strategies, embedding selection, metadata schemas, indexing and re-indexing, vector database operations, and latency/cost trade-offs at scale.
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Establish guardrails for production AI: pre-LLM controls (PII redaction, sensitive-data blocking, prompt-injection defenses), post-LLM controls (hallucination and groundedness checks, policy filters), and observability so failure modes surface before users see them.
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Drive responsible AI practices: bias and safety review, human-in-the-loop patterns where stakes warrant, red-teaming, and alignment with regulatory and compliance requirements relevant to our industry.
Cross-Cutting — Architecture, Coaching, and Leadership
Architecture and Technical Ownership
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Own the technical roadmap for highly complex systems within your mission/track, with influence extending across adjacent domains.
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Lead the design of distributed systems and platform-level capabilities that support feature velocity, reliability, and long-term maintainability.
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Make and document branch decisions on system architecture and approved tech selection within established frameworks.
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Identify systemic constraints, legacy patterns, and platform inefficiencies, and lead modernization initiatives that balance long-term improvements with near-term delivery.
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Define complex test strategies including unit, integration, performance, and AI/model evaluation, and set the quality bar across multiple squads.
Coaching and Talent Multiplication
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Coach engineers across all levels — from junior to principals — on both sides of the role: how to build with AI (prompt and context design, agent orchestration, evaluation, guardrails) and how to work with AI (using coding agents effectively, judging output, maintaining critical thinking).
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Run regular forums (office hours, paired sessions, internal demos, written playbooks, reference implementations) that raise the AI fluency baseline of the entire engineering organization.
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Mentor senior engineers and tech leads on architectural judgment, technical decision-making, and career growth. Provide constructive feedback and engage team members in decisions that affect their work.
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Contribute to internal knowledge-sharing platforms and, where appropriate, external technical communities (talks, blog posts) on the team's engineering practices.
Cross-Functional Leadership and Communication
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Translate technical strategy and AI trade-offs to non-technical stakeholders and senior leadership. Present project proposals, status updates, and architectural recommendations across teams.
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Partner with engineering management on capacity planning, technical hiring, and what "AI-native" looks like in our hiring bar.
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Support the technology vision defined with senior leadership and propel its execution through implementation requirements, design docs, training materials, and reference implementations.
So are YOU our next Senior Principal Software Engineer? You are if you have…
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Extensive experience (12+ years) designing, shipping, and operating large-scale production systems, including distributed architectures and multi-service platforms.
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Experience with multiple LLM providers (Anthropic Claude, OpenAI, open-source via vLLM/Ollama) and orchestration frameworks (LangChain, LangGraph, LlamaIndex, or equivalents).
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Expert-level proficiency in at least one modern programming language (.Net, TypeScript, Angular and ReactNative are preferences) and deep working knowledge of multiple architectural patterns.
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Demonstrated experience leaning heavily on AI to accelerate engineering velocity — using AI coding tools to prototype, implement, debug, review, and iterate on production features. You can show specific examples of where AI changed how you and your team work.
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Hands-on fluency with at least one current-generation coding agent (Claude Code, Cursor, GitHub Copilot, Codex, or equivalent) and with the broader pattern of agentic, tool-using workflows.
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Strong understanding of how to evaluate AI output: writing evals, designing test cases, recognizing failure modes, and maintaining critical judgment when models produce confident-sounding but incorrect work.
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Proven track record of mentoring senior engineers and influencing technical direction across team boundaries.
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Excellent written and verbal communication skills, including the ability to produce architectural documentation that other engineers can act on.
Brownie points if you have...
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Experience deploying AI systems in regulated environments where compliance, data integrity, and auditability are architectural requirements.
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Track record of building or contributing to internal developer platforms, golden paths, or paved roads that scale practices across many teams.
Sounds like you? Click below to apply!
#LI-Hybrid #LI-MM1
