Founding Tech Lead

Role Overview

We are building leadership development infrastructure: the connective tissue that unifies fragmented talent development across organisations and careers. We are pre-seed stage, which means you are joining at the very beginning: no established processes, limited resources, maximum agency to build from scratch.

The technical challenge is unlike standard B2B SaaS. We are building a platform where individuals trust us with their most sensitive professional development data — psychometric assessments, coaching conversations, failure patterns, longitudinal growth records — while enterprises need aggregate insights, cohort analytics, and demonstrable ROI. The architecture must make privacy guarantees structural, not policy-based: hard boundaries that cannot be overridden by configuration, admin access, or future business pressure.

Your role

: Design and build this from the ground up. You will be the first technical hire — the person who makes the foundational architecture decisions that everything else builds on. You will own the entire technical stack, from trust infrastructure to consumer-grade product experience to AI integration to enterprise analytics.

What You Will Build

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Trust infrastructure:

data sovereignty layer — encryption, consent management, access logging, hard boundary enforcement between individual and enterprise data. The foundation everything else sits on. •

Individual product:

viral 10-minute assessment with shareable results, portable development record, private dashboard, data export/deletion. •

Enterprise layer:

cohort analytics, SSO, HRIS connectors, pilot management, ROI reporting. DPDP (India) and PDPA (Singapore) compliance. •

AI coaching:

LLM-powered micro-coaching personalised to assessment profiles. Context-aware escalation to human coaches. Strict data isolation. •

Integration layer:

native API. Webhook architecture for enterprise HRIS, LMS, and ATS systems. The order, the stack, the architecture — yours to decide. We have opinions; you have the final call.

Who You Are

Required Depth •

8–14 years of software engineering experience with at least 3–4 years in a technical leadership role (lead engineer, principal engineer, architect, or CTO at a startup) •

You have built and shipped a product from zero to production users — not inherited a codebase, but made the foundational decisions on architecture, stack, and infrastructure •

Deep experience in at least one of: privacy/security-sensitive platforms (healthtech, fintech, identity), B2B2C products with dual-audience architecture, or data-intensive platforms with complex access control •

Strong full-stack capability: you can build a consumer-grade frontend, design a scalable backend, set up cloud infrastructure, and implement CI/CD — because in Year 1 you will do all of these yourself •

Hands-on with modern AI/LLM integration: you have built products that use LLMs in production (not just experimented), understand prompt engineering, RAG patterns, context management, and cost optimisation

Architecture Thinking

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Privacy-first design:

you think about data boundaries before features. You understand the difference between policy-based and architecture-based privacy guarantees. You default to structural segregation over access control. •

Scale-appropriate decisions:

you will not over-engineer for Day 1, but you will make decisions that do not create technical debt when we need to scale to 50,000. You know when to build and when to buy. •

Product sense:

you care about what users experience, not just what the system does. The Leadership Health Check needs to feel like 16 Personalities, not like an enterprise form. You can make that happen.

Entrepreneurial Mindset

•

Startup-ready:

no team, no codebase, no infrastructure on Day 1. You will build it all. You are energised, not overwhelmed, by this. •

Speed with judgment:

you would rather ship an imperfect v1, learn from real users, and iterate than architect in isolation. But you will not cut corners on trust infrastructure — that must be right from Day 1. •

Risk tolerance:

willing to take a non-traditional compensation structure for the opportunity to build something significant from the ground up.

Nice-to-Have

•

Experience in HR-tech, assessment platforms, coaching platforms, or healthtech •

Background in data science, psychometrics, or applied ML; experience with regulatory compliance engineering (GDPR, DPDP, SOC 2) •

Prior founding engineer or CTO experience at a venture-backed startup

What We Offer

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Intellectual freedom:

greenfield technical opportunity — you choose the stack, the architecture, the approach. The trust infrastructure problem alone is a career-defining technical challenge. Applied AI with real stakes and real users. •

Equity stake available

(meaningful ownership, not token options), earned through performance and mutual fit, not guaranteed upfront. You are co-creating the company, not building someone else’s spec. •

Non-traditional compensation:

founding role at a pre-seed company. The structure reflects high ownership and asymmetric upside. If we succeed, your outcomes could be multiples of a senior engineering role at an established company. Risk is real — do not apply unless you are genuinely excited by this trade-off.

Location, Education & Working Style •

Base: Ideally Mumbai. We want the founding team together. •

Working style: high autonomy, deep work protected, outcomes over hours. You will work closely with the founder and the science, coaching, and commercial leads. •

Education:

B.Tech/M.Tech/MS in Computer Science, or equivalent. The credential matters less than what you have built — show us systems you have designed, shipped, and scaled.

Application Process

We want to see: •

CV + portfolio:

show us what you have built — systems you have designed, products you have shipped, architecture decisions you have made. GitHub, technical blog posts, or system design documents welcome. •

Architecture sketch (300–500 words):

how would you design a system where individual coaching data is architecturally invisible to an employer admin, while still allowing cohort-level analytics? What are the key design decisions and trade-offs?

Ready to build? These roles are not for everyone. We are asking you to tolerate uncertainty and build something from scratch with no guarantees. But if you are energised by that trade-off — if you want to solve the hardest trust, privacy, and AI problems in leadership development rather than add features to someone else’s platform — we want to talk.