Akshun Dogra × FTAPI | (Junior) AI Go-to-Market Engineer (w/m/d) application

How do we scale encrypted outreach
sicher jetzt?
Deploy lifecycle GTM workflows daily.

I build production-grade AI and automation layers—from Clay waterfall enrichment and Gong win/loss call parsing to predictive churn early-warning models—integrated directly into Salesforce. Having previously achieved 80% less manual SDR effort through workflow automation, I would apply that operational discipline to FTAPI's jetzt priorities without forecasting identical metrics.

✉ businessakshun@gmail.com

Building Production AI Workflows Across the Customer Lifecycle

Deploying signal-based outbound and predictive churn models at FTAPI risks data fragmentation across sales, marketing, and customer success without unified CRM hygiene and rigorous benchmarking. This role requires rapidly prototyping production AI workflows—including Clay enrichment, Gong call analysis, and churn warning models—while maintaining clean integration boundaries. I will validate this hypothesis by auditing intent signal scoring, testing waterfall enrichment flows, and evaluating model output reliability in staging before shipping daily production tools to internal revenue teams.

Part marketer,
part engineer.

I don't fit neatly in one box — and that's the point. I design systems that connect data to revenue across paid, organic, automation, and full-funnel GTM intelligence.

📈

Performance Marketing

End-to-end paid media across Google, Meta, and LinkedIn. 36x+ ROAS, $50K+ monthly budgets managed. Demand creation and capture in one unified funnel.

At Ingestro & Theo — cut CPC 20%, raised CTR 30%, and recovered €32.7k in suppressed conversion value & ad waste.

Google Ads

Meta Ads

LinkedIn Ads

A/B Testing

Full-funnel architecture

Demand Creation LinkedIn · Meta

↓

Demand Capture Google Search

↓

Retargeting Display · Social

36x+ Peak ROAS achieved

⚡

Measurement & Experiment Operations

I turn ambiguous growth questions into reproducible measurement, clear experiment design, and documented decisions with human review where it matters.

The point is not more automation. It is a traceable path from evidence to a decision a growth team can defend.

Evidence

Measurement

Experiment

Review

Evidence-to-decision loop

🌐

Evidence

Public signals

→

🔗

Measure

Baseline

→

🧪

Test

Experiment

→

🧱

Review

Decision

LEARN

Keep testing

REVIEW

Check evidence

STOP

Document why

Evidence before scale

Most relevant for FTAPI

🎯

GTM Signal Intelligence

Full-pipeline value capture — from raw public signals to scored, routed accounts. I built an autonomous research agent that turns funding, hiring, ad-presence, and tech-stack signals into a live fit score before a single outbound message goes out.

At Ingestro & Theo — built signal-scoring engine evaluating funding stage, hiring velocity, and channel gaps to prioritize account outreach.

Signal Scoring

Fit Tiering

Evidence Routing

Autonomous Research

Signal-to-pipeline engine

🌐

Scraper

6 Modules

→

💼

Signals

Jobs & Stack

→

🎯

Score Engine

0–100 Score

→

⚡

ABM Page

Auto-Publish

HIGH (80+)

⚡ Auto-Publish & AE Route

MID (55-79)

💬 SDR Approval Review

LOW (<55)

⏳ Nurture / Hold

100% Signal-traceable, zero guesswork

🔍

SEO & GEO

Technical SEO, schema markup, and LLM/GEO visibility strategy. 400% organic growth. Early practitioner of Generative Engine Optimisation.

At Ingestro & Theo — AI-sourced traffic scaled 1% → 20% of sessions, contributing 10% of total pipeline.

SEMrush

GA4

Schema

GEO

Python

LLM visibility strategy

📐

Dynamic Schema

Per-page structured data, LLM-readable

🔎

Prompt Tracking

Peec AI + Search Console gaps

💬

Community Signals

Reddit + LinkedIn citations

⭐

Authority Profiles

G2 + Crunchbase optimised

400% Organic traffic growth

💻

Technical Marketing

Webflow builds, GTM, conversion tracking, and ABM page generation at scale. I build and measure without needing a dev team.

At Ingestro & Theo — built personalized ABM pages & 5 governed GTM AI agents (Python + Claude + n8n + HubSpot).

Webflow

GTM

HTML/CSS

Tracking & measurement stack

Webflow

HTML/CSS/JS

↓ events fire

Google Tag Manager Central hub

↓ distributes to

GA4

FB Pixel

Google Ads

0 Dev team needed

What I'd do in the
first 90 days

I bring hands-on experience designing GTM automations and LLM workflows, previously achieving an 80% reduction in manual SDR effort through workflow automation. Supported by 6+ years across growth, Python, SQL, and automation engineering, I focus on building production-grade tools that eliminate manual reporting and CRM friction across the entire customer lifecycle.

01Days 1–30

Diagnose Signal Triggers and Consent Data Flow — I will audit public script tags alongside Usercentrics consent states and Salesforce CRM records to evaluate how web telemetry connects to downstream pipeline systems for Enterprise (Unternehmen) clients. The deliverable is a diagnostic signal map detailing data hygiene gaps across intent triggers and inbound leads. This diagnostic determines which intent signals are qualified for automated sequence orchestration for sicheren Datenaustausch.

02Days 31–45

Instrument Signal-Based Outbound and Waterfall Enrichment — For FTAPI's jetzt priorities, I will request intent data schema access to establish a signal-based outbound architecture using Clay waterfall enrichment and automated lead scoring rules. The deliverable is an automated pipeline connecting intent signals directly into CRM sequence orchestrations. This instrumentation establishes exact scoring boundaries before pushing enriched contact lists to sales teams.

03Days 46–75

Test Automated Win/Loss and Churn Early-Warning Models — I will request Gong call transcripts and early customer onboarding touchpoints to test automated win/loss analysis and predictive churn warning models. The deliverable is a validated test suite benchmarking LLM extraction precision against historical CRM deal records. This testing phase dictates whether specific prompt templates meet production standards or must be rebuilt.

04Days 76–90

Operationalise Lifecycle AI Workflows for Internal Teams — I will deploy production-grade AI workflows into daily use across Sales, Marketing, and Customer Success while maintaining data hygiene standards required for sicheren Datenaustausch. The deliverable is an operationalized automation layer featuring automated CRM hygiene, handovers, and real-time dashboard reporting. This operational rollout replaces manual reporting handwork with daily production workflows for all internal teams.

💡 Day-90 decision gate

If staging evaluations show that Gong win/loss transcript extractions and Clay waterfall enrichments deliver reliable, low-hallucination outputs into Salesforce, I will scale the AI automation layer across all active outbound and retention workflows; if edge-case data errors create CRM friction across sales and customer success, I will iterate by refining prompt structures and signal scoring logic; if downstream revenue teams report consistent data inaccuracy, I will stop pipeline execution to rebuild the workflow integration boundaries.

I already did some
homework.

Hypothesis: FTAPI is establishing an end-to-end artificial intelligence and automation layer across its entire customer lifecycle to improve go-to-market execution. This audit evaluates the technical stack, workflow requirements, and operational integration needed to support high-impact outbound automation and customer retention systems.

⬡ Observations · FTAPI
Full analysis available on request Fit Score: 100/100 (HIGH_FIT)

ACCOUNT FIT SCORE

100/100 HIGH_FIT

📈

Measurement Stack & Digital — Public digital presence data across 5 inspected public pages reveals observed script tags for Google Analytics / GTM, Meta Pixel, Hotjar, and Usercentrics. While observed scripts indicate front-end tag presence, they do not confirm internal backend data utilization or attribution workflows. Establishing a consent-safe signal collection hypothesis will clarify how web interaction telemetry connects to downstream pipeline systems. Audit tag triggers alongside Usercentrics consent states to validate reliable event capture, enabling compliant data flow into outbound and lifecycle automation frameworks.
⚡

Signal-Based Outbound Architecture — Planned initiatives target building signal-based outbound mechanisms with intent signals, lead scoring, waterfall enrichment, and sequence orchestration across customer lifecycles. Designing these workflows requires defining clear integration boundaries between data sources and execution engines to prevent data fragmentation. Map intent signal sources and scoring rules while collaborating across 2 functions, including engineering and marketing, to prototype automated outreach pipelines. This enables rapid deployment of scalable outbound workflows without creating operational friction across revenue teams.
🔍

Lifecycle Risk & Retention — Key roadmap objectives focus on constructing automated win/loss analysis and a churn early-warning model across customer lifecycles, alongside observed HTML references to Salesforce CRM. Connecting customer interaction data to predictive risk models requires structured signal extraction and consistent data hygiene across systems. Evaluate early customer touchpoints during 1 critical early stage, specifically onboarding, while linking recorded call insights to CRM records. This enables proactive risk identification and structured retention interventions for account management teams.
🎯

AI Productionization & Workflow — FTAPI delivers encrypted data exchange solutions while empowering engineering talent with 1 to 3 years of experience to prototype and iterate production AI workflows. Rapid AI workflow development requires rigorous benchmarking to determine whether early prototypes should be refined or rebuilt. Establish rapid prototyping feedback loops and dashboard reporting to evaluate model outputs before deployment. This enables continuous delivery of high-utility automation while protecting operational stability across core business operations.
✓

FTAPI provides strong foundational security positioning and modern tooling options to build production-grade automated AI workflows effectively.
i

I can share a Loom walkthrough with more detail — just ask.

ℹ

Data Transparency: These are outside-in hypotheses based on verified first-party and public evidence. “Not observed” is not the same as “does not exist”; internal data and stakeholder context would be the first validation step.

Marketing, engineering,
and a lot of borders crossed.

My name, Akshun, means "undestructable" in Sanskrit. That's not a personal brand line — it genuinely shapes how I move. I don't quit on campaigns, goals, or challenges like learning German from scratch after landing in a new country.

I started freelancing in digital marketing at 19 in India — working with Punjabi music artists, Australian businesses, and tech clients before I'd ever left the country. That early pressure to figure things out without a team or a budget is where the technical + creative hybrid came from.

I moved to Nürnberg in 2022 for an M.Sc. at FAU, added an Erasmus semester in Madrid, hit German C1, and picked up enough Spanish to embarrass myself. Outside the dashboards, I run a YouTube channel documenting the unfiltered reality of being an Indian expat in Germany — no filters, real stories.

What I keep coming back to: the best marketing sits at the intersection of creativity, data, and technical execution. Most teams have one or two. I try to bring all three.

🇮🇳

India — Where it started

Started freelancing at 19. Music artists, Australian businesses, tech clients. Taught myself SEO, PPC, and web dev out of necessity.

2016 – 2022

🇩🇪

Germany — The big leap

M.Sc. at FAU Erlangen-Nürnberg. German to C1. Joined an AI startup, built full growth stack from scratch.

2022 – Present

🇪🇸

Spain — The bonus chapter

Erasmus at UPM Madrid. Picked up Spanish, confirmed food in Madrid is genuinely incredible.

Sep 2024 – Jan 2025

▶

On YouTube

The unfiltered reality of moving from India to Germany — visa stress, language barriers, real stories.

youtube.com/@akshundogra →

hover to meet the person behind the marketer →

The man behind all of this.

Akshun Dogra · Technical Growth Marketer

How do we scale encrypted outreach
sicher jetzt?
Deploy lifecycle GTM workflows daily.

Building Production AI Workflows Across the Customer Lifecycle

Part marketer,
part engineer.

What I'd do in the
first 90 days

I already did some
homework.

Marketing, engineering,
and a lot of borders crossed.

Did we just build
something worth pursuing?

This page was built
specifically for FTAPI.

Glad you stopped by,
FTAPI.

How do we scale encrypted outreach sicher jetzt? Deploy lifecycle GTM workflows daily.

Building Production AI Workflows Across the Customer Lifecycle

Part marketer,part engineer.

What I'd do in thefirst 90 days

I already did somehomework.

Marketing, engineering,and a lot of borders crossed.

Did we just buildsomething worth pursuing?

This page was builtspecifically for FTAPI.

Glad you stopped by,FTAPI.

How do we scale encrypted outreach
sicher jetzt?
Deploy lifecycle GTM workflows daily.

Part marketer,
part engineer.

What I'd do in the
first 90 days

I already did some
homework.

Marketing, engineering,
and a lot of borders crossed.

Did we just build
something worth pursuing?

This page was built
specifically for FTAPI.

Glad you stopped by,
FTAPI.