Vice President of Artificial Intelligence

The VP of AI Engineering will lead the technical vision for our multi-product B2B ecosystem. You will be responsible for architecting high-performance Agentic AI workflows, advanced LLM orchestration, and RAG-based systems. Your leadership will drive innovation across three core pillars: global messaging solutions, automated audio intelligence, and sophisticated enterprise document analysis for compliance and regulatory tracking.

KEY RESPONSIBILITIES

• Agentic Framework Strategy: Design autonomous AI agents capable of executing multi-step workflows across messaging and document-heavy business processes.
• Advanced Document Intelligence: Oversee the development of AI engines for complex document analysis, including automated extraction, cross-referencing, and intelligent summarization of sensitive enterprise data.
• LLM & RAG Infrastructure: Architect production-grade RAG pipelines to ensure all AI outputs—from chat responses to document audits—are grounded in factual, private datasets with zero-hallucination standards.
• Audio & Speech Innovation: Lead the ASR (Automatic Speech Recognition) roadmap to transform raw audio into structured, searchable, and actionable business intelligence.
• Scalable B2B Architecture: Ensure the AI infrastructure is multi-tenant, secure, and compliant with global data privacy standards (GDPR/PDP), especially regarding automated document processing.
• Strategic Optimization: Balance state-of-the-art model performance with inference cost-efficiency to maintain healthy unit economics across all product lines.

JOB SPECIFICATION

• Education in Master’s or Ph.D. in CS/AI (Bachelor's with exceptional AI product track record is highly welcome).
• 10+ years in Software Engineering; 5+ years leading AI/ML teams in high-growth B2B SaaS.
• Expert in LLMs and Agentic Frameworks
• Proven track record in NLU & Information Extraction for deep/complex document analysis.
• Hands-on knowledge of ASR, diarization, and noise-robust transcription technologies.
• Advanced implementation of RAG pipelines and Vector Databases (Pinecone, Milvus, Weaviate).
• Strong grasp of Unit Economics (optimizing cost-per-inference and token management).
• Ability to translate complex R&D into scalable, user-centric B2B product features.
• Experience in end-to-end MLOps and ensuring AI compliance with data privacy (GDPR/PDP).
• Proven "Talent Magnet" capable of recruiting and mentoring top-tier AI engineering teams.