Job description

Overview We’re looking for a Senior Principal AI Engineer to provide hands-on technical leadership across complex, production-grade AI systems. This role is for a builder-architect—someone who has repeatedly taken AI systems from idea to production, understands where they fail at scale, and knows how to unblock teams to move faster without sacrificing outcomes. This is not a pure strategy or people-management role. It is a deeply technical builder role with organizational impact. You will define technical direction by writing code, designing systems, and helping the organization stay in builder mode as complexity and scale increase. What You’ll Do Architect and build end-to-end AI systems including LLM orchestration, retrieval layers, agentic workflows, and structured reasoning systems Lead the design of multi-agent and tool-calling systems that operate reliably in production Establish and evolve architecture patterns for scalable, cost-aware, and observable AI applications Drive technical decisions across data modeling, AI pipelines, infrastructure, and APIs Define best practices for evaluation, monitoring, and governance of AI systems in production Mentor senior engineers through design reviews, code reviews, and system-level debugging Translate ambiguous business and domain problems into clear technical strategies Stay ahead of emerging AI techniques and integrate what matters—without chasing hype Core Skills & Experience 12+ years of software engineering experience, with deep hands-on experience building AI/ML systems in production Strong proficiency in TypeScript, React, Go, Python and modern AI frameworks Extensive experience with LLMs, including RAG, tool use, prompt systems, and agentic architectures Proven ability to design and ship large-scale AI systems that run reliably in real-world environments Strong architectural judgment across data systems, AI models, infrastructure, and application layers Deep understanding of AI failure modes: hallucination, drift, brittleness, latency, and cost blowups Excellent communication skills—able to explain technical tradeoffs to both technical and non-technical audiences Track record of shipping systems end-to-end, not just prototypes or research work Builder Mentality (This Is Core to the Role) We are explicitly looking for builders. By “ builder, ” we mean an operating mode, not a title. Builders: Bias toward systems that solve user needs, not perfect abstractions Move comfortably from ambiguity → first draft → iteration → production Optimize for learning velocity and customer impact, not theoretical completeness Are willing to build the entire arc of a system to surface real constraints early Treat quality as something you earn through iteration, not something you gate progress with Understand that the last 10–20% of a system—integration, edge cases, UX, usability, reliability—is where real work happens At the Principal level, being a builder also means: Helping the organization stay in builder mode as it grows Collapsing unnecessary complexity rather than introducing more process Knowing when architectural rigor matters, and when it is premature Pulling promising work across the finish line instead of waiting for “perfect readiness” Modeling speed, ownership, and clarity for other senior engineers Your impact is measured not only by what you build, but by how much faster and more effectively others can build because of you. Preferred Experience (Domain-Flexible Specialties) Knowledge graph architecture, ontology design, or semantic modeling in complex domains Graph databases, graph query languages, or graph ML techniques Hybrid systems combining structured reasoning with LLM-based approaches Entity resolution, schema alignment, or knowledge fusion at scale AI systems requiring explainability, auditability, or lineage tracking Experience building AI systems in regulated or high-stakes domains (finance, healthcare, legal, government) MLOps, evaluation infrastructure, or long-running AI services operating at scale What You’ll Love Owning the technical direction of real AI systems that make it into production Solving hard, ambiguous problems where architecture and execution matter equally Leading through hands-on building, not layers of process Working in an environment that values shipping, learning, and iteration over perfection Having the latitude to shape both systems and how teams build them About Us We are an AI-first company, and we mean that literally. AI is not a feature we bolt on. It’s not a marketing layer. It’s not a roadmap experiment. It is the foundation of how we design, build, and operate. We are building systems where machines do what machines do best: pattern recognition, synthesis, analysis at scale. As well as what humans do what humans do best: judgment, context, trust, and accountability. That means rethinking workflows from the ground up. Not “how do we add AI to this process?” but “how should this process exist in a machine-augmented world?” We care deeply about shipping real systems that work in production. In regulated environments. With real customers. At scale. If you’re excited to help invent the next way software is built and deployed, and to do it alongside a team of deeply pragmatic, AI-obsessed builders, we’d love to talk.

Requirements

Must-haves

What You’ll Do
Architect and build end-to-end AI systems including LLM orchestration, retrieval layers, agentic workflows, and structured reasoning systems
Lead the design of multi-agent and tool-calling systems that operate reliably in production
Establish and evolve architecture patterns for scalable, cost-aware, and observable AI applications
Drive technical decisions across data modeling, AI pipelines, infrastructure, and APIs
Define best practices for evaluation, monitoring, and governance of AI systems in production
Mentor senior engineers through design reviews, code reviews, and system-level debugging
Translate ambiguous business and domain problems into clear technical strategies
Stay ahead of emerging AI techniques and integrate what matters—without chasing hype
Core Skills & Experience
12+ years of software engineering experience, with deep hands-on experience building AI/ML systems in production
Strong proficiency in TypeScript, React, Go, Python and modern AI frameworks
Extensive experience with LLMs, including RAG, tool use, prompt systems, and agentic architectures
Proven ability to design and ship large-scale AI systems that run reliably in real-world environments
Strong architectural judgment across data systems, AI models, infrastructure, and application layers
Deep understanding of AI failure modes: hallucination, drift, brittleness, latency, and cost blowups
Excellent communication skills—able to explain technical tradeoffs to both technical and non-technical audiences
Track record of shipping systems end-to-end, not just prototypes or research work
Builder Mentality (This Is Core to the Role)
We are explicitly looking for builders.
By “ builder, ” we mean an operating mode, not a title.
Bias toward systems that solve user needs, not perfect abstractions
Move comfortably from ambiguity → first draft → iteration → production

Nice-to-haves

Preferred Experience (Domain-Flexible Specialties)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmltypescriptreactgopython

Platform: lever
Effort: quick