Principal Full Stack Engineer

Stratus

5d•Remote

About The Position

The Principal Full Stack Engineer joins our Labs team — the faster-moving, experimental side of Stratus engineering, where new product lines are prototyped, validated, and brought to production. You will be the senior technical voice in the room, setting architectural direction across whatever shape the next bet takes. The work spans multiple frameworks, data stores, and cloud primitives. We care more that you can drop into any of it, read it deeply, and make the right call than we do about which language is on your resume today. Equally important: this role owns the agentic AI surface of what Labs ships. You will design and build the agent loops that sit on top of our products — retrieval, tool use, multi-step planning, evaluation, human-in-the-loop checkpoints, and the production guardrails that keep them safe. We need a technical hands-on engineer who can both deliver the platform and own the agent layer end-to-end. As a Principal Engineer, you will demonstrate deep technical expertise across multiple domains, influence technical strategy, and make high-impact architectural decisions. You will set technical standards and engineering culture that extend beyond your immediate team to the entire organization, and work closely with engineers, designers, and product managers across the SDLC to deliver high-quality solutions.

Requirements

8+ years of professional software development experience with 3+ years in senior/lead/principal roles.
Deep code fluency — can pick up an unfamiliar codebase in any mainstream language and form a defensible opinion in days, not weeks. Language is a tool, not an identity.
Strong working command of both Azure and AWS and the tradeoffs across container orchestration, serverless, messaging, identity, and managed data stores.
Solid command of both SQL and NoSQL systems.
Hands-on Kubernetes experience in production.
Demonstrable AI engineering chops — has shipped LLM-powered features to production, comfortable with retrieval, embeddings, tool calling, structured outputs, and cost/latency tradeoffs.
Has designed agentic workflows end to end — planning, tool use, evaluation, failure-mode analysis, human-in-the-loop. Can articulate when an agent is the right answer and when a deterministic pipeline is.
Proven track record of leading architectural initiatives and influencing decisions across teams.
Strong technical leadership and mentoring experience.
Understands SOLID, DRY, KISS, YAGNI, and TDD and applies them when warranted.
Excellent communication and ability to work collaboratively.
Polyglot — quick to ramp on whatever the problem requires.
Comfortable ramping into our current stack — C# / .NET on the backend, Node.js / TypeScript and VueJS on the frontend — and into whatever Labs ships next.

Nice To Haves

Working familiarity with the Model Context Protocol (MCP), agent frameworks, and eval frameworks.
Experience deploying inference workloads on Azure (AI Foundry / Azure OpenAI) and/or AWS (Bedrock, SageMaker).
Experience with Autodesk products (AutoCAD, Revit, Fabrication, BIM 360 Docs, Forge / APS).
Familiarity with modern serverless technologies on either cloud.
Experience leading large-scale system migrations or architectural transformations.
Solid understanding of security best practices, including the AI-specific surface.
Understanding of event-driven architectures.
Track record of establishing technical standards across engineering organizations.

Responsibilities

Collaborate with cross-functional teams to define, design, and ship new product surfaces emerging from Labs.
Drive architectural decisions and implementations for the team and the platforms it produces.
Design and build production-grade agentic workflows on top of what we ship.
Lead technical standards and best practices that impact multiple teams.
Mentor and guide engineers and influence engineering culture.
Make significant individual contributions to our SaaS product across both Azure and AWS.
Help modernize and consolidate legacy surfaces as integration touch points emerge.
Ensure the performance, quality, and responsiveness of customer-facing applications and the agentic systems built on top of them.
Implement and maintain modern automated testing practices, including evals for AI components.
Advocate for incremental delivery, consistently aligning with the practices of modern high-performing engineering teams.