About the role

We are building a multi-tenant platform that Fortune 1000 L&D; teams use to procure, configure, and deliver premium executive education inside the tools their people already use.

You will own the client HR-tech integration layer that lets Emeritus plug into any customer's identity and learning stack in an afternoon rather than a quarter, together with the product surfaces that sit on top of it — an L&D; Admin console, a Customer Success console, and an embedded Learner experience.

This role requires an L&D; / EdTech / HR-Tech engineering background. The integration layer is the hardest and most differentiating part of this product. We need someone who has already shipped LMS, SSO, and HRIS integrations in production — who knows why a SCIM deprovisioning race condition orphans accounts, what breaks an LTI 1.3 launch, and how completion data actually lands on a learner's LMS transcript — so they can lead this work from day one rather than learn it on the job.

What you'll own

Client HR-tech & LMS integration layer (primary mandate)

LMS integration — LTI 1.3 deep-linking and connectors for Cornerstone, SAP SuccessFactors, Workday Learning, Docebo, Canvas / Instructure, and 360Learning, so Emeritus programmes appear as a native tile inside the customer's LMS.

Completion & records sync — xAPI / LRS event pipelines (enrolled → started → milestone → completed) that post back to the customer's LMS transcript, keeping their system of record and compliance dashboards accurate.

HRIS connectors — employee, role, and org-hierarchy sync against Workday, SAP SuccessFactors, and Oracle HCM to drive cohorts, eligibility, and reporting.

Integration framework — a reusable, standards-first connector framework with tenant-scoped configuration, secret management, observability, and a self-serve setup flow the L&D; Admin can complete without engineering hand-holding.

Consume the platform identity layer (SAML, OIDC, SCIM) built by the platform team, and define the requirements it must meet for each customer stack you onboard.

Full-stack product surfaces

Ship the L&D; Admin, CSM, and embedded Learner experiences end to end — from catalogue configuration, procurement, and approval workflows through cohort management, assignment, and enrolment.

Design the real-time state and notification model that keeps the three personas in sync, so an action in one console updates the others live.

Build clean, well-typed APIs and data models against the platform's shared tenancy and entitlement primitives.





Translate a high-fidelity, brand-precise UI into a performant, accessible production front end.

Quality and scale

Own reliability, security, and performance for the integration layer as it scales from the first customer to many.

Hold the engineering bar on your surfaces: testing strategy, instrumentation against the canonical event contract, and observability.

Integrate the platform's AI intelligence layer (via the Anthropic Claude API) into product surfaces — consuming AI services, not building or training models. What we're looking for

Must-have

6+ years building and operating production web applications as a full-stack engineer.

Direct L&D; / EdTech / HR-Tech domain experience — a hard requirement, not a preference — you have shipped and maintained an LMS, HRIS, or learning-platform integration in production, and can name the systems and the customer scale you ran them at.

LTI 1.3 / LTI Advantage — deep linking, Names and Role Provisioning Services (NRPS), Assignment and Grade Services (AGS), platform-tool key exchange, and launch debugging.

xAPI (Tin Can) with an LRS — statement design, actor / verb / object modelling, and posting completion back to a customer system of record. SCORM 1.2 and 2004 familiarity; AICC a bonus.

SCIM 2.0 and SAML 2.0 / OIDC on the consumer side — you configure, test, and debug these against customer IdPs — Okta, Entra ID, Ping, Google Workspace — even though the provider implementation sits with the platform team.

At least two of Cornerstone OnDemand, SAP SuccessFactors, Workday Learning, Docebo, Canvas / Instructure, or 360Learning integrated in production — including their API quirks, rate limits, and sandbox realities.

React with TypeScript and a contemporary React framework, plus a typed Python backend — our stack is Next.js 14 (App Router), TypeScript 5+, and FastAPI on Python 3.12. Remix, Vite + React Router, or Django REST are accepted backgrounds; we expect productivity in ours within weeks.

REST and webhook integration engineering — OAuth 2.0 client credentials, retries with exponential backoff, idempotency keys,



and reconciliation jobs for eventually-consistent systems.

Agentic AI development tooling in daily use — we use Claude Code; Cursor or equivalent is fine. Nice-to-have

Four or more of the named LMS platforms, or a connector framework others reused.

Operating an LRS yourself rather than posting into a customer's.

GCP experience (Cloud Run, Cloud SQL, Pub/Sub, Redis) and Terraform / IaC.

Experience building against LLM APIs (Anthropic Claude, or similar) in product features.

Payments / billing integration experience (e.g. Stripe). What success looks like — first 90 days

Days 1–30 — Ship your first production integration path — a first LMS connector against a real customer's stack, on top of the platform identity layer.

Days 31–60 — Stand up xAPI completion sync end to end, and shape the reusable connector framework.

Days 61–90 — Make integration setup genuinely self-serve for an L&D; Admin, and set the reliability and security bar for the layer as it scales. Our stack

Frontend: Next.js 14, TypeScript.

Backend: FastAPI, Python 3.12.

Data: PostgreSQL with TimescaleDB, Pub/Sub, dbt.

Infra: GCP (Cloud Run, Cloud SQL, Pub/Sub, Memorystore Redis, Vertex AI Vector Search), Terraform, Workload Identity Federation.

Intelligence layer: Anthropic Claude API.

Tooling: Claude Code as a first-class part of our development workflow.

A note on scope. This is a full-stack product and integrations role. The core identity, tenancy, and entitlement layer is owned by a dedicated platform engineer — you build on it rather than building it. AI/ML model development and applied research are owned by a separate hire; you consume the intelligence layer through well-defined APIs. If either of those is what you're looking for, this isn't that role, and that's by design. How we work

Small, senior, high-trust team. We move fast, we write things down, and we hold a high engineering bar. You'll have real ownership and direct line of sight from your code to enterprise customers going live. We value people who can defend their design decisions clearly, take well-reasoned pushback, and make fast calls once convinced.

We are distributed across India and documentation-first: written decisions, service READMEs, and runbooks carry more weight here than meetings. AI development tooling is a working expectation rather than a preference — we screen for judgment in how you use it, and we evaluate the leverage you get from it.

📌 Senior Full Stack Engineer (India)
🏢 Emeritus
📍 India