Founding AI Engineer
at Google Accelerator Startup Building AI-Marketing Platform.
PythonWe're building in a market that hit $47B in 2026 and is on track to hit $107.5B by 2028 and we're not chasing that opportunity from the sidelines. Our founding team brings marketing expertise from Kotak and Amazon paired with deep AI chops, we were selected as one of just 20 startups by Google, and we already have paying customers like Kotak and Myntra using the product in production. Join us early and help build the infrastructure that turns AI marketing adoption (already at 88%) into real, scaled results for customers who are already paying for it, not case studies.
Founding Product Engineer, Applied AI
Location: Mumbai • Hybrid • Reports to: CTO
WHAT THIS ROLE ACTUALLY IS
Weve built a multi-agent AI system for marketing. It works well out of the box but every client (their data, workflows, brand rules, compliance constraints, output preferences) is different. Generic doesnt ship results. Customised does.
This role is the bridge: you sit between our agent architecture and the client’s reality. You understand both deeply, and you make the agents do what each specific client needs them to do.
Think Palantir Forward Deployed Engineer, but for AI agents. An engineer who owns product outcomes end-to-end.
WHAT YOU'LL DO
Day-to-day split: roughly 60% in Codex and Claude Code shipping customisations, 40% in client rooms understanding what to ship next.
- Customise the agents. Tune prompts, build new agent capabilities, configure workflows, wire up integrations. Codex and Claude Code are your primary IDEs. You ship working code, not specs for someone else to build.
- Own client outcomes end-to-end. From discovery to live deployment, you are the person responsible for whether the agents actually solve the client’s problem. No PM-engineer handoff to hide behind.
- Problem-solve in real time. Client says “the output tone is off for our brand” or “the research agent is missing X data source” — you diagnose, fix, and redeploy, usually within days.
- Hold your own on creative. When a CMO says “this doesn’t feel right,” you can question intelligently — is it the prompt, the input brief, the brand guidelines themselves, or the client’s own confusion? Most engineers reach for the prompt. The good ones diagnose first.
- Feed learnings back to core product. Anything that gets customised 3+ times becomes a platform feature. You decide what graduates.
- Build the playbook. As we scale from 10 to 50 to 100 clients, the customisation patterns you create become the foundation others work from.
WHAT YOU NEED
- Real engineering chops. You can read a codebase, write production code, debug agent failures, and ship to live clients. AI tools are your accelerant, not your crutch.
- Comfort with agents as a system. You understand how LLMs fail, why prompts drift, what context engineering means, when to use tools vs. fine-tuning, why eval harnesses matter. If “MCP,” “tool use,” and “agent harness” feel like jargon, this isn’t the role.
- Client-facing instincts. Comfortable in a room with a CMO, an enterprise IT head, and a junior brand manager — and you can switch register for each in the same meeting. If client calls feel like tax to you, don’t apply.
- Some creative literacy. You don’t need to be a designer. You do need to be able to tell the difference between “this output is technically broken” and “this output is technically fine but creatively wrong” — and chase the right thread.
- Bias toward shipping. A working hack today beats a clean design next quarter.
YOU'LL THRIVE HERE IF
- You’re an engineer who realised products live or die on customisation, not architecture.
- You’ve been frustrated by being kept away from customers.
- You like building with AI tools and want a role where that’s the job, not a side experiment.
- You like marketing as a domain — or you’re willing to fall in love with it.
YOU WON'T THRIVE HERE IF
- You want to optimise systems in a corner with headphones on.
- You think client calls are below your pay grade.
- You think AI is overhyped (we don’t).
- You need a fully scoped ticket queue and weekly sprint rituals.
Shubham Vishwakarma
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Durch das Abschicken meiner Unterlagen für den Rrecruitingprozess erkläre ich mich damit einverstanden, dass meine Daten zum Zweck des Recruitingprozesses verarbeitet und nach Abschluss des Recruitingprozesses für weitere 6 Monate gespeichert werden. Ohne dein Einverständnis können wir deine Unterlagen für den Rrecruitingprozess leider nicht berücksichtigen. Du kannst dein Einverständnis jederzeit widerrufen. Weitere Informationen, wie wir deine Daten verarbeiten findest du in unserer Datenschutzerklärung unter folgendem Link: https://tvarit.com/privacy-policy/
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon