Director of Engineering & AI Product Development

Job Description

Director of Engineering & AI Product Development

\n

\n

Location: San Diego, CA — In Office

\n

Employment: Full-Time

\n

Works closely with: Chief Architect/CTO and Director of Technology Operations

\n

\n

The Mission

\n

\n

Turn our technical architecture and AI vision into working, production-grade software.

\n

This is a hands-on engineering leadership position responsible for building the technology and leading the people who build it .

\n

\n

The operating model is:

\n

Architecture → Engineering Plan → Build → Integrate → Deploy → Improve

\n

\n

The Chief Architect/CTO focuses on architecture, difficult technical problems, AI capabilities, innovation and differentiated intellectual property.

\n

\n

You own turning that architecture into working software.

\n

\n

The Director of Technology Operations independently tracks commitments, verifies releases, manages QA governance and provides operational visibility.

\n

\n

What You Own

\n

\n

Engineering Execution

\n

\n

Own day-to-day engineering execution across the technology platform.

\n

You will:

\n

\n
• Translate architecture into executable engineering work
\n
• Build software directly when appropriate
\n
• Break technical initiatives into engineering deliverables
\n
• Assign engineering resources
\n
• Resolve technical blockers
\n
• Review engineering work and code
\n
• Integrate systems
\n
• Drive deployments
\n
• Troubleshoot production issues
\n
• Improve existing systems
\n
• Refactor or replace weak technology
\n
• Increase engineering velocity
\n

\n

You own how the technology gets built .

\n

\n

AI Product Development

\n

\n

Work directly with the Chief Architect/CTO to turn AI concepts and prototypes into reliable products.

\n

This may include:

\n

\n
• AI agents
\n
• LLM integrations
\n
• Agentic workflows
\n
• Data pipelines
\n
• APIs
\n
• Model integrations
\n
• Internal AI tools
\n
• Customer-facing AI products
\n
• Automation infrastructure
\n
• AI-enabled operating systems
\n

\n

\n

The Chief Architect may prove that something is technically possible.

\n

\n

Your job is to make it production-ready, scalable and usable.

\n

\n

Engineering Team

\n

\n

Build and lead the engineering organization.

\n

You participate directly in recruiting, technical evaluation, hiring, onboarding, management and development.

\n

\n

Engineering personnel report through engineering—not Technology Operations.

\n

\n

Technical Implementation Decisions

\n

\n

Own engineering-level decisions regarding:

\n

\n
• Code structure
\n
• Frameworks
\n
• APIs
\n
• Infrastructure implementation
\n
• Development environments
\n
• Integrations
\n
• Deployment architecture
\n
• Technical debt
\n
• Refactoring
\n
• Performance
\n
• Scalability
\n

\n

Major architectural decisions remain aligned with the Chief Architect/CTO.

\n

\n

Build vs. Buy

\n

\n

For every meaningful capability, determine whether we should:

\n

\n

Build → Buy → Integrate → Outsource → Acquire

\n

\n

Protect technology that creates competitive advantage.

\n

Do not spend six months recreating something we can reliably deploy in three weeks.

\n

\n

What You Do NOT Own

\n

\n

To eliminate ambiguity, you do not own:

\n

\n
• Independent QA approval
\n
• Executive technology reporting
\n
• Project-governance reporting
\n
• Security audits/control verification
\n
• Documentation audits
\n
• Release acceptance
\n
• Operational risk reporting
\n
• Verification that engineering commitments were met
\n

\n

\n

Those responsibilities belong to Technology Operations.

\n

\n

You provide the evidence and technical cooperation required for Technology Operations to independently verify them.

\n

\n

Working Relationship With the Chief Architect/CTO

\n

\n

Chief Architect/CTO

\n

\n

Architecture + AI innovation + difficult technical problems + proprietary IP

\n

\n

Director of Engineering

\n

\n

Engineering + implementation + integration + deployment + engineering team

\n

The objective is to dramatically increase the leverage of the Chief Architect.

\n

\n

First 90 Days

\n

\n

First 30 Days: Understand the architecture, codebase and AI systems. Determine what works, what doesn't, technical debt, engineering gaps and immediate priorities.

\n

\n

By Day 60: Own engineering execution, establish development practices, address weak systems and begin building the required engineering team.

\n

\n

By Day 90: Engineering should be shipping production technology predictably without the Chief

\n

\n

Architect personally driving implementation.

\n

\n
• How Success Is Measured
\n
• Are we shipping working software?
\n
• Is engineering velocity increasing?
\n
• Are blockers resolved quickly?
\n
• Is architecture becoming production technology?
\n
• Are AI systems becoming reliable products?
\n
• Is the engineering team becoming stronger?
\n
• Is code quality improving?
\n
• Are deployments becoming more reliable?
\n
• Are customers using what we build?
\n
• Is the Chief Architect spending more time architecting and less time implementing?
\n

\n

\n

We are looking for a builder who can lead builders.

\n

\n