Director, Engineering (nova)

Iterable Iterable · Enterprise · Atlanta, GA +6 · Engineering

Director of Engineering to lead the Nova organization, focusing on building and operating core AI-driven systems and enabling other teams to develop and ship AI-based features. The role involves setting technical direction for AI-powered product capabilities and shared AI infrastructure, driving end-to-end delivery, and establishing reusable platforms. Requires experience in building and operating production AI/ML systems at scale, with a focus on Enterprise-Grade reliability, Context Engineering, RAG, retrieval-ranking, and evaluation frameworks for grounding AI agents. Experience with LLM-based, agentic or autonomous systems is a plus.

What you'd actually do

  1. Lead the Nova engineering organization across AI application development ML systems
  2. Set technical direction for AI-powered product capabilities and shared AI infrastructure
  3. Drive end-to-end delivery from research and experimentation through productionization and scale
  4. Establish reusable platforms, patterns, and foundations that enable other product teams to build AI features efficiently
  5. Partner with engineering leaders across the company to support adoption of AI capabilities

Skills

Required

  • 10+ years of engineering experience
  • significant experience leading managers and scaling multidisciplinary teams
  • Proven track record of building and operating production AI/ML systems at scale
  • Deep understanding of Context Engineering, including RAG, retrieval-ranking, and evaluation frameworks for grounding AI agents in high-fidelity data
  • Experience defining technical strategy across multiple teams
  • Experience translating emerging AI capabilities into practical, customer-facing agentic workflows that drive measurable business outcomes
  • Ability to navigate ambiguity with a Bias for Action, making crisp decisions to keep the product roadmap fluid in a fast-moving AI landscape and drive alignment across engineering, product, and data
  • Experience operating in a multi-team SaaS environment
  • Strong communication skills and ability to influence senior stakeholders

Nice to have

  • Experience building and productionizing LLM based, agentic or autonomous systems that handle complex orchestration and evaluation
  • Experience establishing shared AI platforms or internal developer enablement capabilities
  • Familiarity with analytics systems, experimentation workflows, or decisioning frameworks

What the JD emphasized

  • Enterprise-Grade reliability
  • Context Engineering
  • Bias for Action

Other signals

  • leading AI-driven systems
  • enabling other teams to develop and ship AI-based features
  • establishing shared foundations
  • defining architectural patterns
  • setting technical standards for applied AI development
  • building and operating production AI/ML systems at scale
  • Context Engineering
  • RAG
  • retrieval-ranking
  • evaluation frameworks for grounding AI agents
  • customer-facing agentic workflows
  • LLM based, agentic or autonomous systems