Open to GTM Engineering & FDE roles in Switzerland & the EU

I build the systems that turn signals into pipeline.

I'm Neeraj, a GTM Engineer and Forward Deployed AI Engineer. I ship scoring engines, AI SDR agents and outbound products — with data models, tests and automation instead of more tools and more noise.

Book a 30-min call →See the work

LinkedIn GitHub admin@neerajsujan.com

Netherlands · EU citizen · English, German, French, Dutch

Currently building

AI agents for revenue teams

systems built end to end, 2 live in production

~20s

to research a lead in Slack, down from 10–15 min

10/10

planted buyers found in 100,000 events

automated tests across the GTM projects

Ready for Swiss teams

EU citizen, four languages, zero paperwork for you.

Based in

Netherlands

CET — same time zone as Switzerland

Right to work

EU citizen (German)

No visa sponsorship needed for Switzerland or the EU

Languages

English · German · French · Dutch

All fluent — currently learning Swiss German

Open to

Switzerland & EU

Ready to relocate to Switzerland

Selected work

Four systems, built end to end. Real code, real numbers.

Signal scoring · Data engineering

Open source

GTM Signal Engine

Scores buying patterns, not single events — fewer alerts, every one worth acting on.

The problem

Revenue teams drown in one-off intent alerts: a page visit, a job change, an ad click. Reps stop reading them and miss the accounts that are actually buying.

What I built

Medallion pipeline (bronze → silver → gold) on DuckDB and dbt, with weights, decay and combo rules in config files RevOps can tune without code.
Pattern multipliers for breadth, channel mix and activity spikes, plus an ICP fit gate, exclusions and a 5-alerts-per-rep daily budget.
Dagster runs it daily and pushes "why now" alerts to HubSpot, Attio and Slack, with a Next.js dashboard on top.

PythonDuckDBdbtDagsterNext.jsHubSpotAttioSlack

GitHub ↗

Animated walkthrough: alerts pile up, then data flows through bronze, silver and gold layers into scored alerts.

10/10

planted buyers surfaced as hot

100k

events across 500 accounts scored

tests (35 dbt + 15 pytest)

AI agent · Sales automation

Open source

Slack SDR Agent

Paste a LinkedIn URL in Slack; 20 seconds later the lead is researched, verified and in the CRM.

The problem

An SDR spends longer researching and logging around each call than on the call itself — and that admin is exactly where follow-ups fall through the cracks.

What I built

/prospect command that enriches profile, company, verified email and AI talking points, then upserts HubSpot without overwriting owners.
One-click call outcomes: no answer starts LinkedIn + email sequences and finds more buyers; picked up drafts a personalised follow-up for approval.
Production ops: idempotent runs, Retry buttons on errors, stuck-work sweeps and a daily summary in Slack.

n8nSupabaseSlackHubSpotProspeoHeyReachInstantlyLLM

GitHub ↗

Slack thread where the SDR Agent posts an enriched lead card with role, verified email, company and research.

~20s

per researched lead (was 10–15 min)

n8n workflows, generated from code

end-to-end checks, all passing

AI SaaS · Founder build

Live · early access

Pipelinewired

An AI agent that finds buyers, writes to each one personally and books the call — built for agencies.

The problem

Agencies stitch together 5–7 tools to run outbound: lead data, signal tracking, sequencer, inbox, dialer, CRM and booking. Nothing ties them into one loop.

What I built

Onboarding agent that reads a website and drafts the offer, ICP and personas; a signal-based lead engine scores every prospect before outreach.
Multichannel sequences across email, LinkedIn and an AI voice agent, a unified inbox with AI-drafted replies, and booking pages.
Agency-native multi-tenancy, a visual workflow builder, compliance controls, Stripe billing and a public API.

Next.jsPostgresAI SDKRetell voiceUnipileStripePlaywright

Live site ↗

Pipelinewired landing page: "Your AI agent finds the buyers and books the call."

40+

product screens shipped

126

test files (unit + Playwright e2e)

scheduled background jobs

Agentic AI · Prompt chaining

Live

Dhaga Returns Intelligence

Classifies 6,500 weekly return reasons without a data team — three-model chain that routes, synthesises and self-evaluates.

The problem

A fashion e-commerce brand processed 6,500 return reasons a week by hand. The categories were inconsistent, the root causes invisible, and every trend report was already two weeks stale by the time anyone read it.

What I built

Haiku classifier that maps each return note to a structured taxonomy in one call; a router that decides whether the case needs synthesis or can be answered directly.
Sonnet synthesiser that groups patterns across batches and drafts weekly trend summaries, with a Haiku evaluator that scores confidence and flags low-quality outputs for human review.
Streamlit portal with a live returns feed, category breakdown, weekly digest and a prompt-by-prompt trace so ops can see exactly what each model decided and why.

PythonClaude HaikuClaude SonnetStreamlitPrompt chainingAnthropic SDK

GitHub ↗

Dhaga Returns Intelligence: Streamlit dashboard showing return categories, weekly digest and model trace.

6,500

return reasons classified per week

model hops per request (classify → synthesise → evaluate)

<2s

per batch classification (Haiku)

Azure ML · End-to-end ML system

Live

Contoso Delay Classifier

Predicts e-commerce delivery delays before despatch — trained on 10,000 orders, deployed as a full observability stack.

The problem

A logistics team had no early signal on which orders would miss their delivery window. Delays were discovered reactively, after the SLA breach and after the customer complaint.

What I built

GradientBoosting classifier trained on 10,000 synthetic orders with class-balanced weighting; baseline without balancing scored 81.9% accuracy by predicting "on time" for everything — AUC rose from 0.559 to 0.589 once the delayed class was weighted correctly.
Azure Functions API that validates inputs, runs inference and writes every request to Cosmos DB with a 30-day TTL; partition key chosen on userId not carrier to prevent hot-partition throughput ceilings.
Operations portal with a health panel (p50, p95, error rate over 15 min), recent-requests table and a load test that confirmed cold-start latency of ~1,400ms dropping to sub-400ms on warm instances.

Pythonscikit-learnAzure SQLCosmos DBAzure FunctionsFlaskVercel

Live demo ↗GitHub ↗

Contoso Delay Classifier portal: prediction form, System Health panel with p50/p95 latency and error rate.

0.589

AUC (vs. 0.559 unweighted baseline)

46%

recall on delayed class (was 0% before balancing)

<400ms

p50 latency on warm instance

Live product · Content engine

Live

Fieldwork

The hub for Forward Deployed and GTM Engineers: roadmaps, live jobs, posts, people, books and repos.

The problem

FDE and GTM Engineering are new fields. The good material is scattered across LinkedIn, YouTube, podcasts and job boards, buried in hype and recruiter posts.

What I built

Ingestion pipeline that builds the catalog from seven sources (Apify scrapers, iTunes, Open Library and curated lists) and never wipes data on a failed run.
Quality filters and AI judgments separate real insight from hiring, announcements and promo; people are ranked on insight posts only.
Live jobs read from 25 company job boards at request time; a GitHub Action refreshes the catalog daily and Vercel redeploys on every commit.

Next.jsTailwindApifyGitHub ActionsVercelTypeScript

Live site ↗

Fieldwork home page: "Engineers who work in the field," with FDE and GTM Engineering tracks.

company job boards read live

160+

curated posts from practitioners

Daily

automated refresh

Announcement card: Neeraj Sujan joins the Founding Cohort of RevTech Academy as a Founding Member.

Credentials & training

Always shipping, always learning from operators.

RevTech Academy ↗ — Founding Cohort

Founding MemberCurrent

Strategy → Context, Process → Architecture, Execution → Orchestration. Partnered with Puzzle and Clay.

Revenue Cartel — Operator Deployment

Sep – Nov 2026

Live 8-week program building six production GTM systems: intent signals, AI research agent, deliverability, personalised outbound, pipeline automation and a capstone.

GTME.SCHOOL ↗ — GTM Engineering program

Sep 2026

ICP definition, TAM/SAM/SOM mapping, buying signals and intent triggers, offer and message-market fit.

How I work

Engineering discipline, applied to revenue.

Start from the rep’s day

Every build starts with the workflow someone actually lives in — Slack, the CRM, the inbox — not with the tool.

Treat pipeline like a product

Data models, tests and idempotent runs. If it breaks at 6 AM, it should retry, alert and recover on its own.

Config over code

Weights, thresholds and rules live where RevOps can change them. Tuning is a config change, not a sprint.

Ship, measure, tighten

Ship the smallest loop that works end to end, then use real outcomes to decide what to build next.

Toolkit

GTM & data

Clay · Apollo · Prospeo · Deepline · HubSpot · Attio · dbt · DuckDB

Outbound & automation

n8n · Instantly · HeyReach · Smartlead · Unipile · Dagster

Claude · AI SDK · LLM evals · Retell voice · Agents & tool use

Engineering

TypeScript · Python · Next.js · Postgres · Supabase · Vercel · Playwright

Writing

Field notes on GTM & FDE work.

GTME FDE All writing →

FDEBuilding a Delay Classifier End-to-End on Azure: What I Learned

Oct 26

FDEHow to Classify 6,500 Return Reasons a Week Without Hiring a Data Scientist

Oct 26

FDEWhy the Real Problem Is Never the One They Ask For

Oct 26

FDEYour AI Worked in the Demo. Here's Why It Dies in Production.

Sep 22

FDEGreat Engineers Freeze in FDE Interviews. Here's the Fix.

Sep 22

Let's talk

Building revenue infrastructure? I'd love to help.

I'm looking for GTM Engineering and Forward Deployed Engineering roles in Switzerland and across the EU. Best fit: B2B SaaS and AI-first teams that want signals, agents and outbound built properly.

Book a 30-min call →Email me LinkedIn