Head of AI Engineering

The Role

We’re looking for a Head of AI Engineering to own the technical direction of Thout.ai’s core AI

systems end-to-end: the multi-pass LLM pipelines, agentic RAG architecture, structured

extraction systems, and the model serving infrastructure underneath all of it. This is a hands-on

leadership role, not a purely managerial one. At this stage, you are the most senior technical

voice on AI: writing code, debugging production issues, and making architecture calls in the

same week you’re setting technical roadmap and hiring the team to execute it. This role

rewards a bias to action, since specs and priorities will shift weekly and the expectation is to

build anyway.

You’ll report directly to the CTO and work closely with the founding team on product direction,

given the stage of the company. Within your first year, you’ll also be building and leading a

small AI engineering team.

Team today: You will be the founding AI engineering hire, working directly with the CTO and

co-founders Team you’ll build: AI/ML engineers, applied research, and eventually MLOps, as

the company scales

What You’ll Own

Technical strategy & architecture

Set the technical direction for Thout.ai’s core AI pipeline: transcript processing, structured

TLDR generation, action item extraction, PII detection/masking, and daily briefing

generation

Own architecture decisions for agentic, tool-using LLM systems (ReAct-style planning, MCP-

based orchestration) with production-grade guardrails, evals, and observability

Design and defend build-vs-buy and model-selection tradeoffs (open-source vs. proprietary,

fine-tuned vs. base) against real cost, latency, and accuracy constraints

Hands-on system building

Design and debug multi-pass LLM pipelines, including subtle production issues like prompt-

cache invalidation from schema injection, and architect around them (stable tool schemas,

client-side structured-output validation, etc.)

Build and maintain hybrid retrieval systems (BM25 + dense, re-ranking, query rewriting)

tuned for meeting transcript dataOwn memory management and context layer architecture: how conversational history,

entities, and prior meeting context persist and get surfaced across sessions, balancing

context window limits against retrieval cost and latency

Build long-context and cross-session memory systems that let the product connect ideas

across meetings over time, not just within a single transcript

Own model serving and inference optimization (vLLM/TGI-class stacks, ensemble base +

fine-tuned model strategies) to keep the product fast and affordable to run at scale

Lead fine-tuning efforts for domain-specific extraction tasks (e.g., PII/NER systems) as a

named product vertical

Team & process

Hire and mentor a founding AI engineering team as the company scales

Establish internal engineering standards: eval pipelines, prompt style guides, structured

output conventions, and configuration practices that the team can build on consistently

Balance startup speed with the rigor needed for a product handling sensitive client data (PII,

confidential meeting content)

Cross-functional partnership

Work directly with the CTO and co-founders on product roadmap, translating business

priorities (e.g., beauty industry client needs) into technical execution

Represent the AI engineering function in strategic conversations, including due diligence,

fundraising technical narratives, and client-facing technical credibility conversations as

needed

What We’re Looking For

Master’s degree in Computer Science, Machine Learning, or a closely related field

8+ years of experience in AI/ML engineering, with demonstrated ownership of production

LLM or NLP systems end-to-end, not just research or prototyping

Deep, hands-on expertise in agentic RAG pipelines, tool-using LLM agents, and production-

grade GenAI infrastructure

Experience designing memory and context layer systems for LLM applications: long-context

management, cross-session state, and context window/cost tradeoffs

Strong systems fundamentals: model serving/inference optimization, fine-tuning (PEFT/LoRA-

class techniques), and structured output enforcement at scale

A track record of debugging non-obvious production issues (cache invalidation, schema drift,

latency regressions) under real operating constraints

Prior experience operating at a Lead or Principal level, ideally with some team-building or

mentorship experience

Comfort with ambiguity and speed inherent to an early-stage company: you’ll set your own

priorities as much as receive them

Nice to Have

PhD in Computer Science, Machine Learning, NLP, or a related field

Published research in NLP/ML (EMNLP, NAACL, EACL, ACL, COLING, or similar), signaling

depth beyond applied engineering

Experience with MCP-based orchestration or multi-agent coordination

Experience building or scaling a technical team from a small founding group

Background in privacy-sensitive or regulated domains (PII handling, data masking,

compliance-adjacent systems)Open-source contributions in the NLP/ML space

Soft Skills & Leadership Traits

Can explain complex AI trade-offs to non-technical co-founders in terms of product and

business impact, not just technical elegance

Treats inconsistencies and edge cases as bugs to fix, not things to work around

Comfortable being the final technical decision-maker with limited oversight

Wants to build a team and mentor, not just stay heads-down as an IC forever

First-principles thinker, willing to question defaults (frameworks, architectures,

“best

practices”) when they don’t fit the actual problem

What Success Looks Like

First 30 days: Deep familiarity with the existing pipeline and codebase; identify the highest-

leverage technical risks; ship one meaningful improvement to an existing system.

First 90 days: Own a core piece of the AI pipeline end-to-end; establish or improve evaluation

and testing practices; have a clear point of view on architecture decisions for the next 6

months.

First 180 days: First AI engineering hire made; measurable improvement in a core product

metric tied to AI quality, whether that’s accuracy, latency, cost, or user-facing output quality.

What You Get

Direct reporting line to the CTO and close partnership with the founding team on product

and technical direction

Ground-floor ownership of the AI architecture for a company built around a genuinely hard,

underserved problem: organizational memory and institutional knowledge loss

Base compensation, performance bonus, and ESOP/RSU participation [full details per

individual offer terms]

The opportunity to build and lead a technical team from its earliest stage

This is a leadership role at a company early enough that the person in this seat will define what

“good” looks like technically, not inherit it. If that’s the kind of bet you want to make, we’d like

to talk.