// project
A native, multiplatform AI career-intelligence engine, and the open-source project it's built on.
The brief
Candidai is a local-first AI career-intelligence engine: resume onboarding, reverse-ATS discovery, evidence-based fit scoring and tailored résumés, in one pipeline. The product idea and scoring model started on career-ops, an open-source Node.js job-search engine (⭐68k+) that I actively contribute to upstream. The native app is a from-scratch Kotlin Multiplatform rebuild: the same A-F fit-scoring engine, ported and verified line-for-line against the original, now running identically on Android, iOS, Desktop, Web and a Spring Boot server instead of a single Node process.
What shipped
// case study
The problem
career-ops's scoring engine lived in a single Node.js process. Proving the same logic could run identically on a phone, in a browser tab and on a server meant more than porting the syntax: it meant the scoring could not quietly drift from the original the moment it moved.
The decision
Port core:engine (A-F fit scoring, ATS search, SimHash fingerprinting, funnel math) as a no-IO module, then verify it against career-ops's own test vectors rather than trust a rewrite by inspection, the same zero-token discipline the upstream project already applies to keeping LLM cost off the hot path.
The result
One verified engine now drives five targets (Android, iOS, Desktop, Web and a Spring Boot 4 server) from 25 Kotlin Multiplatform modules, and 24 of the correctness fixes the port surfaced have already merged back upstream to the public career-ops project.
25 KMP modules · 5 targets
25
KMP modules · 5 targets
45k
lines of Kotlin · 543 files
87
ATS & job-board providers
24
PRs merged upstream
// multiplatform
The real screens (and, where it's live, the running build) per platform — not a mockup.
Real Roborazzi captures: first screenshots off the actual Compose UI, not mockups.
// design notes
A 25-module clean-architecture split, 12 feature modules and 6 core modules, targets Android, iOS, Desktop, Web (wasmJs) and a Spring Boot 4 server from one shared Kotlin codebase: 543 files, ~45,000 lines. core:designsystem, core:protocol, core:engine, core:data, core:network and core:ai sit underneath feature modules for dashboard, pipeline, explore, intel, ops, profile, auth, assistant and more.
core:engine holds the A-F fit-scoring rubric, ATS search, SimHash fingerprinting for duplicate-listing detection, a liveness classifier, and the funnel math. None of it touches the network or disk. It's ported 1:1 from career-ops's original JavaScript implementation and checked against that implementation's own test vectors, so the scoring behaves identically whether it's running on Android, in a browser tab, or on the server.
Room (KMP) plus DataStore caches everything locally over a Ktor REST + NDJSON/SSE sync layer, so the dashboard stays usable offline and catches up when connectivity returns. An agent-interop surface lets other agents, and the OS itself, drive the app without going through the UI. It covers Android AppFunctions, iOS App Intents/Shortcuts, candidai:// deep links, and a documented OpenAPI contract.
Where an LLM adds real value, it runs on-device first: ML Kit GenAI / Gemini Nano on Android, Apple Foundation Models on iOS. Every AI-assisted step has a deterministic-heuristic fallback, so fit scoring and résumé tailoring keep working with zero model available, the same discipline career-ops applies with its zero-token scan path.
The engine's scan path hits Greenhouse, Ashby and Lever APIs plus per-company local parsers directly, at zero LLM cost, falling back to an agent-driven search only for companies with no structured source. Every scanned posting passes through one shared trust-validator that scores and flags it before it reaches the tracker. 87 ATS & job-board provider modules plug into that one contract instead of reinventing trust scoring each time.
career-ops's multi-profile architecture is a profiles.yml registry mapping each candidate to a private data root while sharing one engine install. That is the same shape the native app's per-candidate routing follows: one server, N profiles, a strict User/System data contract between them.
Already running the career-ops CLI? Point Candidai at the existing checkout instead of starting over: the first-run wizard's Profiles screen has a Bring your data step, a doctor gate checks it's a usable career-ops root and says exactly what's missing if not, then imports it as an active profile. Reference mode leaves the folder where it is so the CLI can keep reading and writing it, guarded by a cross-process tracker lock; copy mode clones it onto the server volume for a host with no shared filesystem. The same path is exposed over HTTP for a server deploy.
Any mutating request may carry an Idempotency-Key header; a retry with the same key on the same method and path is acknowledged without re-executing the write, the exact contract the mobile offline outbox itself relies on. Mutating requests to the API are rate-limited per client IP, with read-only GETs left unlimited. The scan and explore endpoints stream progress over SSE (text/event-stream) rather than blocking until a scan completes, so a caller sees offers arrive live instead of waiting on one long response.
24 merged pull requests against the public career-ops repository (⭐68k+, independently verifiable): two new ATS providers (BambooHR, Breezy HR), a dashboard rendering fix that rewrites only the changed Status cell instead of the whole row, an agent-inbox feature for queuing requests across sessions, an opt-in LLM relevance re-ranker for the pipeline, and a long run of correctness fixes. Most target one class of defect: code that reports success while doing the wrong thing. Distinct non-Latin company names collapsed to one key and silently deleted a tracked application; a `$` sequence in CV text spliced the template into the résumé while the build exited 0; a date filter was ignored in its `--flag=value` form, so a bounded scan silently ran unbounded; concurrent adds to the agent inbox dropped queued requests with no error; an unlocked append to shared scan history could interleave and corrupt it; and a `k`/`M`/`B` magnitude suffix walked an inflated claim straight past the fact-checker that exists to stop exactly that. Each shipped with a runnable reproduction and a regression test proving the fix.
// architecture
// under the hood
// directions
Pick a treatment to see it whole. Where one ships a dark counterpart, drag the divider to compare the two faces of it — same layout, same content, only the light changes.
// gallery
Swipe or use the arrows · tap a screen to enlarge
frequently asked
SDE-2, Android & Product Owner at Dice.tech (June 2023 - Present), platform owner of the app behind 50k+ monthly active users (22k+ daily, platform owner at Dice.tech). See the source.
Took GPS accuracy to 95%: up from 50%, by predictive dead reckoning. See the source.
80% crash reduction: Crashlytics + structured concurrency fixes. See the source.
~87% of the UI layer: 455k of 523k UI-layer LOC, verified screen by screen against the legacy XML. See the source.
B.Tech, Computer Science & Engineering, NIT Bhopal (MANIT) (2017 - 2021). See the source.
Consulting Engineer, Platform & AI at Neev Consulting (April 2026 - Present). Built the LLM assistant layer of an ERPNext/Frappe consulting ERP: business-context resolution, capability discovery, and an AI capability gate that defaults OFF with a test proving it. Models client to project to PO to milestone to GST invoice to payment end to end. See the source.
Doori: Offline-first mileage, travel & expense tracker on one Kotlin codebase across Android, iOS, Wear OS, watchOS & Desktop. See the source.
Gaddi: A Hinglish social-deduction bluffing game of power, satire & second chances. Gaddi ke liye kuch bhi karega. See the source.
PaymentsLab-KMP: An Integration Lab for the Android payments ecosystem: every gateway behind one abstraction, with a live look at what actually happens on each transaction. See the source.
Candidai: A native, multiplatform AI career-intelligence engine, and the open-source project it's built on. See the source.
Yes: The Loopdown, his field-notes writing hub, where the recurring bug characters are named after real production incidents. See the source.
Answered by his own teammates, not by him: see the EB Profiles, one question per member, in their own words. See the source.
Open to remote (worldwide / India) and hybrid in Pune / Bengaluru. Email siddharthpandalai990@gmail.com. See the source.
Shared Kotlin Multiplatform libraries used across his own apps, plus merged upstream pull requests on career-ops: see the repos and the running count. See the source.
Yes: see the Play Store shelf for the apps that are actually live and installable, not just source. See the source.
Every route on the site, as a tile you can open: 3D builds, labs and canvases, not screenshots. See the source.
