LATAM Principal Software Engineer (AI)

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Requirements:

• Expertise in programming AI solutions
• Experience building, training, and deploying AI agents; adept with ML frameworks (e.g., TensorFlow, PyTorch) and cloud-based AI services.
• Demonstrable proficiency with all of the following: building attractive user interfaces, building data-driven REST APIs, administering databases/cloud/servers.
• 8-12 years professional experience in relevant technology and projects, including value engineering, system architecture, and software estimation, as well as technical leadership with DevOps and Cloud-Based deployment.
• Hands-on experience architecting AI solutions at scale—setting up end-to-end pipelines, handling large datasets, and optimizing models for production environments.
• Experience presenting to client stakeholders and communicating technical and business value. Ability to articulate complex AI concepts to both technical and non-technical audiences, demonstrating the ROI of AI-driven initiatives and aligning them with client objectives.
• Experience understanding and articulating the business outcomes that your development projects impact
• Experience with coaching and mentoring, demonstrated through ad-hoc collaboration and structured code reviews.
• may include intensive programs, Bachelor's Degree in Computer Science, Software Engineering, or an equivalent experience

Responsibilities:

• Lead development teams to ensure sprint commitments are met
• Foster a collaborative environment where team members can experiment with and learn AI/ML concepts—and openly share knowledge about best practices and emerging technologies.
• Drive the design and implementation of AI solutions, including data processing pipelines, AI/ML model deployment, and the creation of AI agents aligned with user and business needs.
• Establish AI testing and validation best practices (e.g., performance metrics, data quality checks, model explainability) to maintain high standards for model accuracy and reliability.
• Provide clear direction on AI project scope and feasibility, ensuring alignment between business requirements, data availability, and model complexity.
• Mentor developers on AI coding standards, framework usage (e.g., TensorFlow, PyTorch, Hugging Face), and best practices for integrating intelligent functionalities.
• Propose new processes and habits to streamline the delivery of our engagements.
• Conduct recruiting interviews and analyze candidates to ensure successful hiring/contracting.