The Role

The AI Engineer is part of the Engineering AI team within CT CTO Mexico and is responsible for designing, developing, and scaling AI solutions that help engineers work faster, improve quality, and increase productivity across the product development lifecycle. This role transforms engineering challenges into practical AI assistants, agents, and automation solutions that deliver measurable business value while maintaining the highest standards of quality, safety, security, and responsible AI. Additionally, the AI Engineer serves as a technical mentor and subject matter expert for junior team members.

What You'll Do
(Impact Areas)

AI Solution Design & Development

Design, build, and deploy AI assistants and intelligent agents that support engineering activities such as requirements analysis, design reviews, test creation, and technical documentation. Develop solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agent frameworks, and automation technologies. Create scalable architectures that combine AI capabilities with traditional software engineering approaches. Develop maintainable, secure, tested, and version-controlled code, prompts, and configurations.

Rapid Prototyping & Innovation

Convert engineering ideas into working prototypes within days. Build reusable AI components, templates, connectors, and frameworks that accelerate future development. Conduct demonstrations, pilot programs, and user validation sessions with engineering stakeholders. Evaluate prototype feasibility and guide go/no-go decisions using data and business value assessments.

Quality, Evaluation & Continuous Improvement

Design and execute evaluation frameworks to measure AI solution performance. Monitor key quality metrics including accuracy,



consistency, useful output rates, and false-alarm rates. Conduct validation, regression testing, and performance assessments following engineering quality standards. Continuously improve deployed solutions through user feedback, monitoring, and analysis.

Responsible AI, Security & Compliance

Ensure AI solutions operate under human oversight and maintain engineers as final decision-makers. Document testing methodologies, limitations, version history, and quality evidence. Maintain compliance with information security, intellectual property, and responsible AI requirements. Support audit readiness through proper documentation and traceability.

Collaboration, Adoption & Change Management

Partner with engineering teams to identify opportunities for AI-enabled productivity improvements. Co-develop success criteria, requirements, and adoption strategies for AI solutions. Deliver user training, workshops, and enablement activities. Work closely with Data Engineers, IT teams, product development groups, and leadership stakeholders.

Business Value & Technical Leadership

Contribute technical assessments for business cases, including feasibility, cost, effort, and projected value. Support production deployment activities in collaboration with IT and infrastructure teams. Optimize operational costs of AI solutions.



Mentor junior engineers and promote best practices, standards, and knowledge sharing across the organization.

What Success Looks Like

Key Performance Indicators (KPIs): Rapid delivery of prototypes from approved ideas to functional solutions. AI solutions achieving agreed targets for accuracy, quality, consistency, and usefulness. High adoption rates and active usage across engineering teams. Successful expansion of AI solution capabilities based on validated performance. Reduced engineering effort through automation and AI-assisted workflows. Cost-effective operation and maintenance of AI solutions. Increased reuse of shared frameworks, templates, and components.

Strategic Outcomes

Measurable productivity improvements across engineering teams. Trusted AI assistants and agents integrated into engineering workflows. Strong alignment between AI initiatives and business objectives. Sustainable AI governance through responsible AI practices and quality controls. Successful transition of validated prototypes into scalable production solutions.

Behavioral Indicators

Recognized as a trusted technical advisor for AI-related initiatives. Demonstrates ownership, accountability, and execution excellence. Makes data-driven decisions supported by evidence and measurable outcomes. Builds strong collaborative relationships with stakeholders across functions. Actively mentors others and promotes continuous learning within the team.

Core Competencies

Technical Competencies

- AI and software engineering expertise
- Large Language Models (LLMs), RAG, and AI agent architectures
- Python and SQL development
- Cloud-based AI platforms and deployment
- Software testing and evaluation methodo

📌 AI Engineer (Monterrey)
🏢 Clarios
📍 Monterrey