About Top of Mind Labs :

Top of Mind Labs builds AI systems for private equity firms, their portfolio companies, and mid-market businesses. Our clients are investment firms, wealth-management platforms, legal technology providers, and software companies who need production AI that works on their actual data and workflows.

Our mission is to give every company a brain that remembers everything and uses it. In practice, that means building harnesses for knowledge workers - the lawyers, accountants, analysts, and engineers who do the highest-value work inside these organizations - so that the systems they use hold context, retrieve it accurately, and act on it.

We are a small, senior team. Engineers here work directly with client stakeholders, own systems end to end, and ship to production inside four - to - six week engagement cycles.

What you will build :

- Agentic assistants embedded in business surfaces. Agents that live in Slack and Microsoft 365, read across email, documents, and internal systems, and take action - drafting, reviewing, scheduling, and preparing work product for the human in the loop.

- Document intelligence pipelines. Extracting structured, verified data from dense legal and financial documents, with confidence scoring and human review workflows where accuracy is non-negotiable.

- Retrieval systems over domain corpora. RAG applications for tax, legal, and financial-analysis use cases, where a wrong or ungrounded answer is worse than no answer.

- Semantic layers and knowledge graphs. Ontology design and entity resolution over enterprise data so that downstream agents can reason across systems rather than one database at a time.

- Full applications. Internal platforms that replace spreadsheets and manual processes - data model, API, front end, analytics, and deployment.

You will not be training foundation models. You will be building reliable systems on top of them.

Core requirements :

- 4+ years building and shipping production software, with at least 1 - 2 years on LLM-based applications

- Strong Python. Working proficiency in TypeScript

- Demonstrated experience building LLM applications in production - not prototypes, not notebooks. You have dealt with latency, cost, failure modes, and users who found the edge cases

- RAG systems end to end : chunking strategy, embedding selection, hybrid search, reranking, grounding and citation

- Structured output and tool use : JSON schema enforcement, function calling, validation and repair loops

- Agent development using LangGraph, LangChain, or an equivalent framework - tool use, multi-step planning, state and memory management

- Solid SQL and data modeling; comfort with a modern warehouse (Snowflake, BigQuery, or similar)

- API design, authentication, background jobs and queues

- Enough infrastructure ability to deploy and operate what you build : Docker, CI/CD, logging, cost and latency monitoring

Strongly preferred :

- Evaluation experience - you have built test sets, LLM-as-judge pipelines, or regression suites for non-deterministic systems, and can explain how you knew a change was an improvement

- Integration work against Slack APIs, Microsoft Graph, or Google Workspace APIs

- MCP (Model Context Protocol) server or client development

- Document extraction from complex PDFs : OCR, vision-language models, layout-aware parsing

- Knowledge graph or ontology work - RDF/OWL, property graphs, Neo4j, entity resolution

- React or Next.js

- Open-source contributions to agent frameworks, inference tooling, or LLM libraries. This is the single strongest signal on a resume for this role - include links

- Exposure to open-weight models and self-hosted inference (vLLM, Ollama)

- Domain background in financial services, legal, accounting, or investment workflows

How you work :

- Client-facing communication. English fluent in writing and on calls. You will join client working sessions, demo your own work, and explain technical tradeoffs to non-technical stakeholders

- Comfortable with ambiguity. Requirements change mid-sprint. Scope arrives as a conversation, not a ticket

- Judgment over volume. We use AI tooling heavily across our own development. What we need from engineers is architectural judgment, taste, and the ability to tell when a system is actually working

Our hiring process :

- Application review - 2 - 3 days

- Intro call (30 min) - role, background, mutual fit

- Paid work sample (6 - 8 hours, compensated at market rate) - a real problem drawn from past engagement work. You keep the code

- Technical deep dive (60 min) - walkthrough of your work sample and your prior systems

- Final conversation with the founder (45 min)

- Offer

We aim to complete the full process within two weeks.

New hires begin with a paid four - to - six week trial engagement on live client work before converting to a permanent role.

Why this role :

- Direct ownership. Small team, no layers between you and the client problem

- Range. Agents, retrieval, extraction, knowledge graphs, and full-stack delivery - usually within the same quarter

- Compensation set against global rather than local benchmarks, for engineers who clear the bar

- Employment through an employer of record with full statutory benefits