Director of AI

Engineering

About Cadre AI

The Role

What You'll Do

Engineering Leadership and Team Development

• Manage and develop a team of 3 to 5 Colombia-based engineers, owning their growth, performance, and retention.
• Set the quality bar for the team through code review standards, architecture patterns, and delivery practices that Staff and mid-level engineers build on.
• Run structured 1:1s, provide direct feedback, and build growth plans that make your engineers promotable, not just productive.
• Partner with the Head of AI Engineering on team hiring, capacity planning, and how the Colombia team scales over time.

Delivery Ownership Across Engagements

• Own end-to-end delivery across multiple client engagements simultaneously: timelines, quality, dependencies, and risk.
• Identify delivery risks early and resolve or escalate before they reach the client or the Head; protect the team from scope creep without losing client trust.
• Maintain clear visibility into what every engineer on your team is building, why it matters, and where it stands relative to client commitments.
• Ensure handoffs between engineers, engagements, and phases are clean, documented, and do not create rework.

Technical Direction and Architecture

• Stay hands-on with the most complex or highest-stakes builds in the portfolio: agentic systems, multi-step pipelines, integrations with core client infrastructure.
• Drive architecture decisions for your team's engagements, with awareness of how those decisions affect maintainability, cost, and the client's ability to operate the system after delivery.
• Contribute to Cadre's internal technical standards: what goes into the production toolkit, how we evaluate new models and frameworks, and what patterns we build on across engagements.

Strategic Partnership with the Head of AI Engineering

• Serve as a primary thought partner on engineering strategy: team structure, technical direction, tooling investments, and capability gaps.
• Represent the engineering team's perspective in planning conversations, flagging what is technically feasible, what requires more resourcing, and what trade-offs the business is making.
• Own internal initiative: documentation standards, internal tooling, cross-engagement learning, and engineering process improvements.

Client Collaboration

• Join client calls and strategy sessions where engineering context is required, walking stakeholders through system designs, trade-offs, and build sequencing.
• Translate ambiguous client requirements into concrete delivery plans your team can execute against, and flag scope or feasibility issues before they become commitments.
• Build client confidence in Cadre's technical execution through clear communication, reliable delivery, and proactive risk management.

What You Need to Succeed

• 10+ years of software engineering experience, including 3+ years building LLM-powered systems (RAG, agents, structured extraction, evaluation) in production environments at a consulting firm, agency, or client-services organization.
• Proven people management experience: you have directly managed engineers, given hard feedback, run performance conversations, and made your team measurably better as a result. You want direct reports as a core part of your role, not an add-on.
• A track record of owning delivery across multiple concurrent client engagements: you know how to manage timelines, protect quality under pressure, and keep stakeholders informed without losing the team's trust.
• Expert-level Python, NodeJS and deep full-stack fundamentals, with sound judgment on when to reach for new technology and when boring and reliable wins.
• Production cloud experience (AWS, GCP, or Azure): deploying, monitoring, and maintaining systems that clients operate daily, not just launching them.
• Deep working knowledge of the modern AI stack: prompt and context engineering, evaluation, embeddings, vector search, agent orchestration frameworks, and LLM API integration.
• The communication range to move between a code review, a 1:1 with an engineer, a client architecture walkthrough, and a strategic planning sessions without losing precision or credibility in any of them.
• Consulting-environment adaptability: comfort switching between client contexts, industries, and codebases, and the ability to hold context across multiple engagements without losing detail.
• Based in Colombia with reliable overlap with US business hours for client and team collaboration.
• An AI-first working style: you use these tools daily to multiply your output, and you build that expectation into your team.

Why Cadre AI

• Real scope. You own your engineering team in Colombia, with direct influence over how the team is built, how it works, and where it goes. This is not a coordination role.
• AI-native culture. We do not just build AI for clients. We use it to run our own operations. You will work with people who are as obsessed with the tools as you are.
• Access to the frontier. Through partnerships with Anthropic and OpenAI, you will be among the first to experiment with new models and capabilities.
• Upside. We are bootstrapped, profitable, and growing fast. Early team members share in the success they help create.
• No bureaucracy. Clear accountability. The best idea wins, regardless of who says it.