AI Lead

Position Summary:

Our partner is an established hospitality organization strengthening its internal digital product and engineering capabilities, with live products and production AI agents already in place. The next step is making AI a dependable part of how engineers build software, with clear evidence that it improves delivery and quality.

As AI Engineering Lead, you’ll help shape that transition alongside the Head of Engineering and technical leaders in Product Engineering and SRE/DevOps. The immediate focus is the engineering lifecycle, from understanding requirements and writing code to testing, reviewing changes, and operating software in production.

You’ll bring strong software engineering judgment to questions that don’t have simple answers: where agents can act independently, where human review matters, and how to measure whether a workflow actually helps. There’s room to deepen existing capabilities, establish practices other engineers trust, and grow the AI function over time, with customer and business applications becoming part of the broader scope.

Experience and Education:

• BS in Computer Science, Information Technology, Software Engineering, or equivalent experience/field
• 8+ years of software engineering experience, including technical leadership and recent hands-on development with advanced Python.
• At least 2 years building and deploying LLM applications or AI agents in production, supported by strong architecture, API design, testing, and CI/CD experience.
• Experience introducing AI-assisted development workflows adopted by engineering teams, with measurable impact and clear quality controls.
• Direct experience building, configuring, deploying, or operating agent solutions using Google Agent Platform.
• Hands-on MCP integration development, along with experience evaluating, securing, and monitoring production AI systems.

Skills and Strengths:

• AI-Assisted Software Development
• Google Agent Platform
• MCP Integration Development
• Python
• Agent Workflow Design
• Software Architecture
• LLM Evaluation
• Technical Leadership
• Engineering Workflow Adoption
• API Design
• Automated Testing
• Code Review
• CI/CD
• AI Security
• Human-in-the-Loop Controls
• Retrieval-Augmented Generation
• Cloud Deployment
• Production Monitoring
• Technical Documentation
• Engineering Performance Measurement

Primary Job Responsibilities:

• Define practical standards for AI use across requirements, design, coding, review, testing, documentation, deployment, and operations.
• Build and roll out an AI-assisted engineering workflow that teams use consistently, with quality controls and measurable delivery outcomes.
• Develop reusable agent workflows that address recurring engineering needs.
• Build and extend agent solutions using Google Agent Platform.
• Develop MCP integrations that connect agents to engineering tools, repositories, internal systems, and approved data sources.
• Establish human review and approval boundaries based on the actions agents perform and their potential impact.
• Create evaluation datasets and automated checks to assess agent outputs and detect regressions.
• Use human evaluation where needed to assess correctness, usefulness, and behavior.
• Assess changes to prompts, models, and workflows before introducing them into production.
• Partner with Product Engineering and SRE/DevOps leaders to incorporate AI capabilities into development and delivery practices.
• Work with Data and Engineering leaders to define trusted data access and production deployment controls.
• Establish monitoring for agent reliability, output quality, failures, and operating costs.
• Coach engineers, document reusable practices, and use team feedback to improve adoption.
• Help grow the AI function and extend proven capabilities into customer-facing and internal business applications over time.