Founding AI Systems Lead

Ark Abroad is building an AI-native operating system for a service business.
We're not looking for someone to build another chatbot.
We want someone who can take messy business problems, understand how the company actually operates, and turn them into reliable AI systems that remove work, improve decisions, and make a 20-person company operate with dramatically more leverage.
This is a builder + technical leader role.
You will still write code. You will prototype. You will debug. You will ship.
But you will also decide what should be built, how systems fit together, what should not be built, and eventually lead a small team around those systems.
What You'll Own
1. Make our CRM AI-native
Zoho is our operational source of truth.
Your job is to make AI capable of actually operating on top of it.
Examples:
Agents that read CRM context before acting
Automatically summarising calls and updating records
Detecting risks, missing actions and operational failures
Triggering workflows based on conversations and behaviour
Surfacing the right information to sales/coaching/operations teams
Allowing humans and agents to interact with the same underlying operational data
We don't want isolated AI tools.
We want AI embedded into how the company operates.
2. Build Ark's organisational brain
We have years of:
founder knowledge
coaching conversations
sales calls
SOPs
research
Loom videos
ChatGPT/Claude conversations
customer interactions
outcomes and failures
We want to turn this into a governed intelligence layer that agents can reliably reason over.
You'll work on:
ingestion pipelines
canonical knowledge
RAG/retrieval
memory
knowledge graphs where appropriate
source attribution
evaluation
knowledge updating/deprecation
context engineering
The objective isn't “chat with our documents.”
The objective is for organisational knowledge to become usable infrastructure for agents.
3. Build real agents
Examples include:
learner research agents
candidate positioning agents
proof-of-work agents
coaching preparation agents
accountability agents
sales intelligence agents
pre-onboarding interview agents
voice agents
QA/compliance agents
internal operations agents
These agents need to:
understand → retrieve → reason → act → update systems → measure outcomes.
Not just generate text.
4. Voice AI
We expect voice agents to become an important interface across the company.
Potential use cases:
inbound sales
outbound lead reactivation
learner onboarding
learner check-ins
customer research
escalation interviews
You should understand or be capable of quickly learning:
STT/TTS
real-time streaming
telephony
latency
tool calling
conversation state
CRM integration
call evaluation
5. Evaluate the AI stack
New models, frameworks and AI products appear constantly.
We don't want to chase all of them.
You should be able to answer:
What should we build internally?
What should we buy?
Which model should we use?
Does LangGraph/custom orchestration/etc. actually help here?
Does this need an agent at all?
What is the expected ROI?
What is the cheapest reliable architecture?
What breaks when this goes from 20 users to 2,000?
You will act as the technical filter between hype and business value.
6. Lead a small high-output team
Over time you'll manage engineers/automation builders working underneath you.
But this is not a “delegate everything” position.
We expect the technical leader to remain close enough to the code and architecture to:
review implementations
unblock engineers
prototype difficult pieces
establish standards
debug failures
make architecture decisions
train the team
increase the team's engineering velocity
The Person We're Looking For
You are probably someone who has been a:
Founding AI Engineer
Founding Engineer
Applied AI Engineer
Senior/Staff AI Engineer
AI Engineering Lead
AI Systems Architect
Principal Engineer at a smaller company
Titles don't matter much to us.
Evidence does.
You should be able to show us things you have actually built.
Strong signals
Built production AI systems end-to-end
Strong Python/backend fundamentals
Agent/tool-calling systems
LangGraph or equivalent orchestration
RAG/retrieval systems
Vector databases
Evaluation frameworks
APIs/webhooks/event-driven systems
PostgreSQL
Cloud deployment
observability
CRM/SaaS integrations
voice AI experience
Claude Code/Cursor/Codex-style AI-assisted development
startup or zero-to-one experience
Even stronger signals
You can point to something and say:
“I designed this, built the first version, shipped it, watched it fail, fixed it, and eventually got other engineers building on top of it.”
That is much more interesting to us than certificates or knowing every AI buzzword.
What We Don't Want
We're probably not a fit if:
you only manage engineers
you primarily make architecture diagrams
you've never shipped AI into production
most of your experience is notebooks/models/research
your “agents” are essentially prompt chains
you need perfectly written product requirements before starting
you optimise for sophisticated architecture instead of working systems
you're uncomfortable operating in ambiguity
How You'll Be Measured
Not by the number of AI projects launched.
By outcomes:
hours of human work removed
operational errors reduced
response time improved
decisions improved
adoption by the team
reliability
cost per workflow
measurable commercial impact
The goal is simple:
Build AI systems that make the organisation materially more capable.
You will work directly with the founder and have substantial ownership over how the company's AI infrastructure evolves.
Comp : ₹25-30LPA

Work arrangement
Yes