Job Description

The Role:

We are looking for a Principal Solution Architect with strong Artificial Intelligence experience to lead the vision, strategy, and implementation of enterprise-level AI solutions. The selected candidate will define the AI architecture, establish best practices, and guide the development of scalable machine learning systems that drive innovation and digital transformation across the organization.

Responsibilities:

• Define and lead the AI architecture vision and strategy across the organization.

• Establish standards, best practices, and architectural guidelines for AI solutions.

• Design end-to-end AI/ML architectures based on business and technical requirements.

• Collaborate with technology and data leaders to align AI strategy with the broader data strategy.

• Guide engineering teams in the implementation of scalable AI solutions.

• Identify opportunities for innovation through AI and advanced analytics.

• Lead initiatives to improve data quality and generate actionable insights.

• Support digital transformation by integrating AI capabilities into enterprise platforms.

• Troubleshoot complex architecture issues and continuously improve system design.

• Contribute to technical documentation and architectural governance.

Requirements:

• 5+ years of experience as a Solutions Architect.

• Minimum 5 years of software development experience.

• At least 3 years working on AI/ML projects.

• 5+ years of experience in Machine Learning.

• 5+ years programming in Java, Python, Spark, and Hadoop.

• Must reside in Mexico City (CDMX) — required.

• Experience designing RESTful APIs.

• Strong grasp of supervised, unsupervised, and reinforcement learning; neural networks, deep learning, NLP, and computer vision.

• Experience building, training, evaluating, and deploying AI/ML models.

• Knowledge of MLOps practices and model optimization techniques.

• Hands-on experience with frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Knowledge of LLMs, recommendation systems, and anomaly detection.

• Containers and orchestration (Docker, Kubernetes) on cloud platforms (AWS, Azure, or GCP).