Assistant Vice President/Vice President - Technology/AI & NLP

About the Company:

We are building an AI-first dubbing platform that localizes audio-visual content into 30+ languages without losing emotion, timing, or cultural nuance.

Our edge is not just models - it's the fusion of advanced AI (speech, NLP, and multimodal systems) with human creative intelligence to deliver studio-grade results at global scale. We work with creators, studios, and enterprises that cannot afford quality loss.

Role:

We are hiring a VP - Technology to architect, build, and scale our core AI and NLP platform from zero to global scale.

This is a hands-on leadership role, not a people-management-only position. You will write architecture, review models, make hard tradeoffs, and build the team that ships production-grade AI systems.

What You'll Own:

- Architect and build the end-to-end AI platform powering multilingual dubbing (speech, NLP, alignment, voice, inference pipelines)

- Take systems from prototype - production - scale, with real customers and real SLAs

- Lead and grow a high-performance AI / ML / backend engineering team

- Make core technical decisions: model selection, build vs buy, infra, latency vs quality tradeoffs

- Partner directly with Founders, Product, and Business to translate vision into shipped systems

- Ensure platform reliability, scalability, data security, and compliance from day one

- Establish engineering culture, standards, and execution discipline

What We're Looking For:

- 4+ years of hands-on software engineering experience (real systems, not toy projects)

- Proven experience building AI / NLP products from scratch, not just optimizing existing ones

- Strong understanding of ML systems in production (training, inference, deployment, monitoring)

- Experience leading and scaling engineering teams - hiring, mentoring, and holding high bars

- Comfortable operating in ambiguity and making irreversible technical decisions

Strong Plus:

- Experience with speech, audio, NLP, or multimodal AI systems

- Prior startup or 0- 1 product experience

- Experience scaling AI systems under cost, latency, and reliability constraints

- Exposure to enterprise or media workflows