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 outputsfrom chat responses to document auditsare 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 inMaster'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 (GPT-4, Llama 3) and Agentic Frameworks (LangChain, CrewAI, AutoGen).
- 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.
