Principal AI Engineer -Enfi

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