Head of AI Engineering

Head of AI Engineering

Location: Colombia

Employment Type: Full time

Location Type: Remote

Department: Admin & Support Functions

Overview

Application

Job Description

Head of AI Engineering – Bilingual English C1

Full-Time | Remote | Colombia

About the Company

We are an AI strategy and integration firm that builds production AI systems for B2B companies in private equity, wholesale lending, real estate, and SaaS.

We work with high-growth businesses globally, helping them move revenue, compress costs, and automate the work that used to take entire teams — through AI systems that are built to last and designed to perform.

About the Role

We are seeking a Head of AI Engineering to own all engineering delivery at our Colombia engineering hub.

This is the senior-most technical leadership role in the organization. You will lead a high-performing team of AI, ML, MLOps, and Data Engineers while setting the technical bar personally.

You’ll translate ambitious client goals into scalable, production-ready AI systems — and build the processes, platforms, and culture needed to operate at scale.

The ideal candidate has a proven track record of deploying AI systems that moved real business KPIs, is comfortable being client-facing, and knows what it means to own the whole problem — not just manage a piece of it.

Key Responsibilities

• Engineering Delivery & Leadership
• Own all engineering deliverables — quality, scalability, and on-time execution
• Lead and manage the full engineering organization: AI Engineers, ML Engineers, MLOps, and Data Engineers
• Establish and run Agile/Scrum processes: sprints, backlog grooming, ticketing, and release planning
• Build a culture of accountability, experimentation, and continuous improvement
• AI/ML Strategy & Architecture
• Define and standardize best practices across the full AI/ML lifecycle — from experimentation through deployment and monitoring
• Architect and oversee a full ML platform: infrastructure, scalability, reliability, and rapid iteration
• Design and build scalable, end-to-end ML solutions for predictions, recommendations, search, and growth systems
• Evaluate experiments, document findings, select winning approaches, and roll them out team-wide
• Technical Execution
• Contribute directly to system architecture and key technical decisions
• Oversee model training, optimization, deployment, and performance monitoring
• Drive data excellence: clean pipelines, strong governance, and actionable insights
• Establish frameworks that integrate generative AI into engineering workflows
• Client Engagement
• Join client calls as a senior technical voice, shaping solutions and building stakeholder trust
• Partner with product managers and business leaders to translate business objectives into technical roadmaps
• Present trade-offs clearly so clients understand what’s being built and why it will move their business
• Team Development
• Hire, coach, and mentor top-tier AI-first engineers and engineering leaders
• Establish clear career paths, feedback loops, and performance standards
• Scale the AI function across MLE, MLOps, and Data Engineering as the company grows

Requirements

• 10+ years of engineering experience with significant and proven leadership responsibility
• Track record managing large engineering teams in fast-moving, high-accountability environments
• Deep expertise in AI/ML systems — generative AI, model training, and production deployment
• Strong command of Agile/Scrum and the operational discipline to run a high-output engineering org
• Experience architecting and scaling ML platforms and data infrastructure
• Comfortable being client-facing and translating complex technical concepts into business value
• English proficiency at C1 level or above

Nice to Have

• Former founder or early engineering leader who has operated with full ownership of a technical function
• Experience in consulting, professional services, or client-facing technical leadership
• Track record of deploying AI systems that moved a business KPI — revenue gained, cost saved, or time eliminated
• Active in the AI community — writing, speaking, or building in public

Skills

• Machine Learning, Generative AI, and model optimization
• MLOps, CI/CD, and ML lifecycle management
• Cloud infrastructure (AWS, GCP, or Azure)
• Data engineering, pipelines, and analytics
• Scalable system design and architecture
• Exceptional communication across engineering, client, and executive audiences