Head of AI Engineering

The RoleWe’re looking for a Head of AI Engineering to own the technical direction of Thout.ai’s core AIsystems end-to-end: the multi-pass LLM pipelines, agentic RAG architecture, structuredextraction systems, and the model serving infrastructure underneath all of it. This is a hands-onleadership role, not a purely managerial one. At this stage, you are the most senior technicalvoice on AI: writing code, debugging production issues, and making architecture calls in thesame week you’re setting technical roadmap and hiring the team to execute it. This rolerewards a bias to action, since specs and priorities will shift weekly and the expectation is tobuild 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 asmall AI engineering team.Team today: You will be the founding AI engineering hire, working directly with the CTO andco-founders Team you’ll build: AI/ML engineers, applied research, and eventually MLOps, asthe company scalesWhat You’ll OwnTechnical strategy & architectureSet the technical direction for Thout.ai’s core AI pipeline: transcript processing, structuredTLDR generation, action item extraction, PII detection/masking, and daily briefinggenerationOwn architecture decisions for agentic, tool-using LLM systems (ReAct-style planning, MCP-based orchestration) with production-grade guardrails, evals, and observabilityDesign and defend build-vs-buy and model-selection tradeoffs (open-source vs. proprietary,fine-tuned vs. base) against real cost, latency, and accuracy constraintsHands-on system buildingDesign 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, balancingcontext window limits against retrieval cost and latencyBuild long-context and cross-session memory systems that let the product connect ideasacross meetings over time, not just within a single transcriptOwn 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 scaleLead fine-tuning efforts for domain-specific extraction tasks (e.g., PII/NER systems) as anamed product verticalTeam & processHire and mentor a founding AI engineering team as the company scalesEstablish internal engineering standards: eval pipelines, prompt style guides, structuredoutput conventions, and configuration practices that the team can build on consistentlyBalance startup speed with the rigor needed for a product handling sensitive client data (PII,confidential meeting content)Cross-functional partnershipWork directly with the CTO and co-founders on product roadmap, translating businesspriorities (e.g., beauty industry client needs) into technical executionRepresent the AI engineering function in strategic conversations, including due diligence,fundraising technical narratives, and client-facing technical credibility conversations asneededWhat We’re Looking ForMaster’s degree in Computer Science, Machine Learning, or a closely related field8+ years of experience in AI/ML engineering, with demonstrated ownership of productionLLM or NLP systems end-to-end, not just research or prototypingDeep, hands-on expertise in agentic RAG pipelines, tool-using LLM agents, and production-grade GenAI infrastructureExperience designing memory and context layer systems for LLM applications: long-contextmanagement, cross-session state, and context window/cost tradeoffsStrong systems fundamentals: model serving/inference optimization, fine-tuning (PEFT/LoRA-class techniques), and structured output enforcement at scaleA track record of debugging non-obvious production issues (cache invalidation, schema drift,latency regressions) under real operating constraintsPrior experience operating at a Lead or Principal level, ideally with some team-building ormentorship experienceComfort with ambiguity and speed inherent to an early-stage company: you’ll set your ownpriorities as much as receive themNice to HavePhD in Computer Science, Machine Learning, NLP, or a related fieldPublished research in NLP/ML (EMNLP, NAACL, EACL, ACL, COLING, or similar), signalingdepth beyond applied engineeringExperience with MCP-based orchestration or multi-agent coordinationExperience building or scaling a technical team from a small founding groupBackground in privacy-sensitive or regulated domains (PII handling, data masking,compliance-adjacent systems)Open-source contributions in the NLP/ML spaceSoft Skills & Leadership TraitsCan explain complex AI trade-offs to non-technical co-founders in terms of product andbusiness impact, not just technical eleganceTreats inconsistencies and edge cases as bugs to fix, not things to work aroundComfortable being the final technical decision-maker with limited oversightWants to build a team and mentor, not just stay heads-down as an IC foreverFirst-principles thinker, willing to question defaults (frameworks, architectures,“bestpractices”) when they don’t fit the actual problemWhat Success Looks LikeFirst 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 evaluationand testing practices; have a clear point of view on architecture decisions for the next 6months.First 180 days: First AI engineering hire made; measurable improvement in a core productmetric tied to AI quality, whether that’s accuracy, latency, cost, or user-facing output quality.What You GetDirect reporting line to the CTO and close partnership with the founding team on productand technical directionGround-floor ownership of the AI architecture for a company built around a genuinely hard,underserved problem: organizational memory and institutional knowledge lossBase compensation, performance bonus, and ESOP/RSU participation [full details perindividual offer terms]The opportunity to build and lead a technical team from its earliest stageThis 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 liketo talk.