Responsibilities

• Technology Strategy & Roadmap: Define the multi-year technology strategy and architecture roadmap for AI integration into the GTM stack, focusing on maximizing business value in areas like intelligent automation, predictive forecasting, and generative AI-assisted selling.
• AI Solution Architecture: Architect and establish company standards for advanced AI customer solutions, demonstrating viability through proof-of-concepts.
• Lead Go-To-Market Engineering to scale pipeline generation and seller productivity through AI, automation, and data-driven execution.
• Team Leadership: Lead and develop a global, multi-disciplinary team comprising AI Solutions, full-stack, and SaaS developers, cultivating a collaborative, inclusive, and innovative culture.
• Strategic Impact: Drive significant business impact by leading large-scale, multi-organizational, or global initiatives.
• AI Governance and Ops: Drive AI ops culture to drive continuous evolution of all GTM applications.
• Performance and Optimization: Monitor, analyze, and optimize the performance, cost-efficiency, and operational health of all deployed AI models and solutions, driving continuous improvement through data-driven insights.

Requirements

• Exceptional people-management skills, acting as a Player/Coach to inspire and foster desired behaviors.
• Outstanding verbal and written communication skills, coupled with executive presence.
• Strong enterprise communication, business acumen, and financial understanding.
• Proven track record of thought leadership (e.g., technical articles, conference speaking, open source contributions).
• Extensive experience with Go-to-Market (GTM) applications, particularly the Opportunity to Quote lifecycle.
• Excellent analytical skills and a demonstrated ability to translate detailed data analysis into actionable strategic insights to drive customer adoption and provide business recommendations.
• Demonstrated ability to work effectively across internal and external organizations, building consensus and driving results.
• Proven ability to reduce organizational friction and implement scalable mechanisms.
• Agentic AI & Generative Workflows: Autonomous Sales & Commerce Agents: Proven experience building agentic workflows using frameworks like LangChain , LlamaIndex , AutoGPT , or CrewAI to automate multi-step commercial processes (e.g., automated RFPs, dynamic quote generation, automated lead nurture).
• RAG (Retrieval-Augmented Generation): Deep expertise in architecting high-accuracy RAG pipelines using vector databases ( Pinecone , Milvus , Weaviate , Qdrant ) to ground LLMs in internal product catalogs, pricing rules, and sales collateral.

Preferred

• Master's Degree or PhD in Engineering or a related STEM field is preferred.
• 15+ years of experience in Product Application Engineering.
• Expertise in enterprise AI productivity tools and platforms.
• Modernization Mindset: Proven experience in leveraging AI and automation to modernize governance. Demonstrated success in transitioning programs from manual, spreadsheet-based processes to automated, self-healing platforms.