Vice President AI and Engineering

About the Company

ProductSquads was founded with a bold mission: to engineer capital efficiency through autonomous AI agents, exceptional engineering, and real-time decision intelligence. We’re building an AI-native platform that redefines how software teams deliver value—whether through code written by humans, agents, or both. Our stack combines agentic AI systems, ML pipelines, and high-performance engineering workflows. This is your chance to build not just models, but systems that think, decide, and act. We’re developing AI fabric tools, domain-intelligent agents, and real-time decision systems to power the next generation of product delivery

About the Role

In this role, you will lead large, multi-disciplinary engineering organizations spanning AI, product engineering, data platforms, cloud infrastructure, and security.

You will define and evolve the AI and engineering roadmap, guide the development of AI assistants and agent-based systems, and ensure engineering teams operate with a strong product mindset and platform leverage. The role combines strategic ownership with hands-on technical leadership, requiring deep judgment in AI systems, platform architecture, and organization design.

Responsibilities

• Platform & Product Ownership

• You will own the evolution of shared AI platforms, reusable components, and foundational abstractions that power multiple AI-enabled products and solutions.
• Success in this role is measured by platform maturity, reuse, speed of productization, and engineering leverage created across teams.

• AI & Product Engineering Leadership

• Lead AI and product engineering across multiple domains and problem spaces.
• Own architecture and delivery for AI assistants, copilots, and agent-driven systems.
• Define multi-year AI and engineering roadmaps aligned to long-term product strategy.
• Establish architectural standards, engineering governance, and quality bars.
• Ensure teams operate with product roadmaps rather than project plans.

• AI-First Product & User Experiences

• Own AI-native UX architecture including conversational interfaces, guided workflows, and proactive intelligence.
• Establish reusable design systems and component libraries for AI-driven products.
• Partner with product and design leaders to translate user intent into intuitive AI experiences.

• Enterprise AI Platforms & Architecture

• Architect and scale AI pipelines for LLM orchestration, routing, and serving.
• Build and evolve RAG systems, embeddings, vector databases, and retrieval layers.
• Design agent orchestration frameworks including intent recognition, tool selection, and multi-step execution.
• Implement AI observability, evaluation frameworks, and feedback loops to ensure quality and trust.
• Ensure platforms are modular, secure, multi-tenant, and extensible.

• Agentic Systems & Responsible AI

• Own conversational memory, contextual continuity, and long-horizon task execution.
• Define and enforce guardrails, safety systems, and responsible AI practices.
• Balance experimentation with production-grade reliability and governance.

• Cloud, Infrastructure & Security

• Own cloud-native infrastructure strategy for AI and product workloads.
• Ensure scalable systems for model serving, workflow orchestration, data pipelines, and observability.
• Champion security, privacy, compliance, and reliability-by-design across all systems.

• External Signal & Ecosystem Impact

• Ingest real-world usage signals to continuously refine platform abstractions and product direction.
• Act as a technical thought leader representing ProductSquads’ AI and engineering direction in selective external forums.
• Ensure platforms evolve based on broad applicability rather than single-use customization.

• People Leadership & Organization Design

• Build and mentor senior engineering leaders including Directors and Principal Engineers.
• Create a culture of ownership, accountability, technical excellence, and learning.
• Define clear career paths, leadership progression, and performance frameworks.
• Lead globally distributed teams with clarity, trust, and high expectations.

Qualifications

• 10+ years of progressive engineering leadership experience.
• 5+ years leading AI/ML, platform, or full-stack engineering teams.
• Proven experience scaling enterprise-grade AI or data platforms.
• Deep expertise in LLM systems, RAG, agent frameworks, model serving, and AI observability.
• Strong experience with cloud-native distributed systems and AI-native user interfaces.