Description:
Company: BraindAI
Location: Remote (India preferred)
Type: Full-time
Compensation: Competitive, plus potential equity
The TL;DR
We ship production AI systems for B2B clients, and we want them shipped faster and better than a team ten times our size could. Making that happen is your job.
You lead the client projects, build the machine that makes delivery fast and reliable, and lead a small team to run it. Hands-on enough to architect and debug anything yourself, senior enough to own the entire technical side of client delivery. Not just an engineer, not just a strategist: the rare person who bridges both.
🎯 Sound like you? Apply through our form here:
https://braindio.notion.site/3163d76ce91a80b98c8ed12296a06b7a?pvs=105
Every application goes through the form, nothing else gets reviewed.
About BraindAI
A three-pronged AI company:
1. Consulting. Helping businesses figure out where AI actually makes sense, and where it doesn't.
2. Custom Solutions. Production AI systems for clients: workflow automation, AI agents, RAG, LLM integrations.
3. Systems. Our own internal AI tools that make the team more efficient.
Our clients are B2B companies (primarily UK/Ireland) who need AI that actually works, not demos or POCs, but production systems that handle edge cases and deliver results.
The Role
You own AI engineering for our client delivery business. It comes down to three things:
- Lead the projects. Own technical direction on client work end to end: scope, architecture, and making sure it ships and holds up in production. Real B2B clients with real operations riding on the result.
- Build the machine. Stand up the workflows, guardrails, SOPs, and tooling that let a small, senior team deliver at a level that normally takes 5 to 10 people.
- Lead and build a 10x team. Hire, level up, and direct a lean pod where 5 people do the work of 50, multiplied by the machine you build for them. This takes day-to-day delivery off the founders.
You turn a 4-week project into a 4-day sprint, not by working 18-hour days but with Claude Code workflows, AI agent teams, and systems thinking that multiply output.
Time split:
- 55% Architecture and direction
- 20% Framework and internal tooling
- 15% Hands-on building the critical paths
- 10% Research and staying ahead (mandatory)
What success looks like in your first 6 months
- The delivery machine runs without the founders in the loop.
- Quality holds or improves as we take on more work, with the team staying lean.
- Your reusable tooling has measurably cut the time to ship a typical project.
Hit that and you are not just a hire. You are the person running delivery engineering and growing it from here.
What You'll Work On
Client solutions: workflow automation, AI agents (sales, ops, comms), RAG and document Q&A, LLM integrations, data enrichment pipelines.
Internal leverage: our own AI tools, reusable frameworks, and testing/guardrail systems that let the team ship fast without breaking production.
The meta-work: AI agent teams that work alongside you, testing frameworks that make AI-built solutions production-safe, and reusable architectures for common patterns.
The Tech Landscape
Broad role. You should be the person the whole team turns to on almost any technical question: deep engineering fundamentals with serious AI fluency on top. No fixed stack, you shape what we use.
AI and LLMs (your core): frontier and open models with a point of view on when to use which; RAG, embeddings, and memory; agentic systems (tool use, orchestration, evals); fine-tuning and prompt design; keeping pace as the frontier moves.
AI-assisted development: AI coding environments and agents as daily drivers; custom agent teams and automated workflows that do real engineering; MCP, computer use, and whatever lands next.
Engineering fundamentals (the wizard part): full-stack range; system and architecture design that survives real load; data engineering at scale; cloud, infra, and reliable production; security, reliability, and cost as first-class concerns.
Automation and integration: workflow platforms plus custom engines, wiring AI into the tools a business already runs on.
We don't care about a brand-name stack. We care about genuine range: deep enough to architect and debug anything, broad enough to be the technical answer for the team, fluent enough in AI to bring that edge to every problem.
Who You Are: The 10x Engineer
You're not 10x because you code faster. You're 10x because:
- You build systems that let others ai-code safely. Guardrails and tests built in, so juniors ship fast without breaking things.
- You automate your own workflow obsessively. Claude Code workflows, AI agent teams, custom tooling your past teams probably adopted.
- You think \"what makes this problem disappear forever?\" You build once to solve categories of problems, not instances.
- You're proactive. You see issues before they become issues.
Leading a 10x Team
Not a solo-wizard role. You lead a small team and make it punch far above its size:
- Build the machine they run on. Workflows, agents, guardrails, and SOPs that turn each engineer into the equivalent of several.
- Direct the work. Who builds what, what gets AI-generated versus hand-written, where senior attention goes.
- Grow the people. Set the bar, review work, unblock, and pull them up instead of doing it all yourself.
- Win on leverage, not headcount. 5 people delivering what would take 50.
You measure yourself by how much your team can build because of what you built for them.
➡️ Still reading? Good sign. Apply through the form here before you forget:
https://braindio.notion.site/3163d76ce91a80b98c8ed12296a06b7a?pvs=105
The Bridge Person
Most engineers fall into one of two traps:
- The Pure Technical: solid execution, but thinks one layer deep and needs detailed specs.
- The Pure Strategic: great ideas and big picture, but can't actually ship.
You're both. You can zoom out to the 6-month vision and zoom in to the bug on line 47 in the same conversation. You architect and execute.
Your Information Diet
You follow both sides of the AI world.
Deep tech: Karpathy, Simon Willison, Swyx, the Anthropic and OpenAI engineering blogs, LangChain for agent architectures.
Builder/business: Greg Isenberg, Y Combinator, the \"vibe coder\" movement (you know its limits), indie hackers and build-in-public. You read the deep-dives and the business breakdowns, and you know both matter.
What You're Already Using
Non-negotiable, you already live this way.
Daily: Claude Code / Codex, AI coding assistants as extensions of how you think, custom workflows and prompts.
Experimented with: MCP servers, computer use, local LLMs, multiple providers (with opinions on tradeoffs), the latest releases within 48 hours of launch.
Opinions on: what's overhyped, what's underrated, when not to use AI, Claude vs GPT vs open source.
What You've Done Before
- Shipped 2-3+ production AI systems with real users and real debugging.
- Built tools for yourself that made you faster, and that your team adopted.
- Architected solutions other engineers built out.
- Debugged AI systems when they hallucinated or failed.
- Set up AI-assisted dev workflows: Claude Code configs, custom agents, automated testing.
- Made real architecture calls: RAG vs fine-tuning, model selection, build vs buy.
Green Flags
GitHub with real projects (not forks and tutorials), continuous experimentation, something you built to solve your own problem, startup or small-team experience, some technical writing.
In conversation: gets excited explaining how things work, asks clarifying questions, says \"I don't know, but here's how I'd find out,\" has opinions but updates with new info, and is curious about the business and not just the tech.
Red Flags
- Tutorial engineer: only course/hackathon experience, can't explain decisions, falls apart on edge cases.
- Buzzword engineer: right terms, no depth, defensive when questioned.
- Blinders-on engineer: solves what's in front of them, never asks why, needs specs for everything.
- AI-assisted interview risk: perfect answers with 4-5 second delays, can't explain live.
What You'll Get
- Compensation: competitive and based on experience, plus potential equity in the business.
- Ownership: you own AI engineering, with real decisions and responsibility, plus potential equity so you share in what you build.
- Interesting problems: varied client work, greenfield internal product.
- Speed: small team, no bureaucracy, ship weekly.
- Remote: work from anywhere, India timezone preferred for overlap.
- Growth: a founding-level seat. Prove the model and you run delivery engineering end to end as we scale.
How To Apply
You must apply through the form to be considered.
We only review applications submitted through the form. Emails, DMs, and generic resumes will not be looked at.
🚨 Apply here: https://braindio.notion.site/3163d76ce91a80b98c8ed12296a06b7a?pvs=105
The form is not a formality. It asks you to show how you think and work, so take the time with it. We move fast, and you'll hear back within 48 hours.