Unusual6mo ago

Senior Principal AI Engineer -Enfi

United States·BostonFull Timesenior

Data ScienceMachine Learning EngineerAI EngineerDataData & AI

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Quick Summary

Overview

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…

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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.

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

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

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

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

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.

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

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.