Principal Product Engineer - Evinova

About this role

The Role We are looking for a pragmatic builder-architect — a senior engineer who ships fast without leaving a mess, and makes architectural choices that hold up as the product scales. This is a hands-on technical leadership role: roughly 40% writing code and prototyping, with the remainder spent on architecture, mentoring, and raising the engineering bar within your team.

You will embed with a product team for extended periods, owning technical direction and building AI-powered features end-to-end — from idea through production. You won’t just advise; you’ll build, and what you build will set the pattern for others. You may end up managing some engineers.

Most engineers lean one way. “Hackers” ship fast but accrue debt; “architects” build clean abstractions but stall on delivery. You are both. You know when to prototype loosely and when to invest in the durable version — and you can articulate why.

What You’ll Do • Design and build AI-powered product features — agent architectures, RAG pipelines, model orchestration, evaluation frameworks, and guardrails — with the same engineering rigor as any production system: testable, observable, gracefully degrading.

• Own the full stack for the features you build — application code, data, infrastructure — making end-to-end decisions about deployment, observability, cost, and security.

• Make architectural choices that optimize for reversibility early and durability when the problem is actually understood.

• Mentor and coach engineers on your team, transferring judgment and mental models, not just answers. Calibrate involvement to stakes: get out of the way for cheap-to-reverse work, lean in for load-bearing decisions.

• Read existing systems as accumulated knowledge before treating them as debt. Understand why things are shaped the way they are before proposing changes.

• Identify and manage the blast radius of technical decisions — the dangerous ones at this level aren’t bad deployments, they’re bad directions.

What We’re Looking For

Engineering Judgment • You think in failure modes and second-order effects, not happy paths and demos. “Who inherits this, and what does it cost them if I’m wrong?” is a question you ask naturally.

• You optimize for sustainability — testability, clear boundaries, sane defaults, documentation — so what you build can be owned and extended by others.

• You treat constraints as the design problem. You map what’s frozen, what’s validated, what other systems depend on, and what can’t take downtime before proposing solutions.

AI Engineering • You have built and shipped AI-powered features in production — not just used AI tooling for personal productivity.

• You treat AI systems as engineering problems: versioned, evaluated, observable, and designed to degrade gracefully when models behave unexpectedly.

• You use AI as a force multiplier on judgment you already have — it accelerates the parts you understand well, precisely because you can evaluate the output.

• You use AI to compress the learning loop, not skip it. You build real mental models of new technology, using AI as an accelerant, not a crutch.

Working with Teams • You transfer judgment, not just answers. You surface reasoning, install mental models, and make yourself progressively less necessary.

• You lead through demonstrated competence, not positional authority — and you know that doing the work yourself is sometimes the failure mode.

• You learn the team’s context, constraints, and history before injecting opinions. You earn trust by understanding what came before.

Learning • You learn to a depth proportional to the decision. Evaluating something? Defensible opinion, move on. Committing the product to it? Deep enough to understand failure modes and sharp edges.

• When you pick up new technology, you’re trying to understand why it works the way it does and what problem its designers were solving — because that’s what transfers.

Technical Environment Our stack spans the following. We don’t expect mastery of all of it — but given the role, you should be able to pick up almost any of it quickly. • Cloud: AWS (primary) — architecture and infrastructure

• Front-end: React, TypeScript, Vite, Tailwind, shadcn/ui, BlockNote, Nginx

• Back-end: Node.js, Next.js, Python, Kafka, FastAPI, Dramatiq + Valkey (task queue), PynamoDB (single-table DynamoDB), SQLAlchemy async + asyncpg (PostgreSQL), Prometheus, MongoDB

• Infrastructure: Docker, Kubernetes (EKS), AWS CDK (TypeScript), ECS Fargate, DynamoDB, S3, RDS, Elasticache Valkey, Bedrock, Secrets Manager, SSM

• DevOps: NX, pnpm, GitHub Actions, GHCR, Docker/Buildx, Wiz, GitHub OIDC

• AI Infrastructure: Google ADK, LiteLLM, OpenRouter, Bedrock (KB, BDA, Guardrails), MCP, Langfuse + OpenTelemetry, RAGAS, specialist domain agents

• AI Tooling: Claude Code, Cursor, Copilot, opencode, Hermes Agent

Requirements • Bachelor's Degree

• Minimum 8+ years of experience in software engineering, with meaningful time spent at a senior/staff/principal level owning technical direction.

• Production experience building AI-powered features — agent systems, RAG, model orchestration, or similar. Not just prompting or fine-tuning in isolation.

• Strong full-stack capability — comfortable across application code, data stores, and infrastructure. You don’t need to be an expert in all three, but you can’t treat any of them as someone else’s problem.

• Experience with AWS at scale — you’ve designed, deployed, and operated production systems on AWS, not just used it for personal projects.

• Demonstrated ability to mentor and elevate other engineers — through pairing, design review, or informal technical leadership.

Nice to Have • Experience in regulated industries (healthcare, pharma, finance) where compliance constraints shape technical decisions.

• Background building multi-agent or agentic AI systems in production.

• Familiarity with infrastructure-as-code (CDK, Terraform, Pulumi) and container orchestration (EK

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