Senior AI Engineer

Role Overview

• Design, build, and deploy generative AI applications using Google Gemini (Pro, Ultra, Flash), PaLM 2, and other Google-hosted foundation models via Vertex AI.
• Implement Retrieval-Augmented Generation (RAG) architectures using Vertex AI Search, Vector Search, and document embedding pipelines for enterprise knowledge retrieval.
• Develop multi-modal AI capabilities leveraging Gemini's vision, text, and code understanding for hospitality use cases such as guest experience, analytics, and operations.
• Build and maintain agentic AI workflows and orchestration using LangChain, LlamaIndex, or Google Agent Builder — integrating tools, APIs, and enterprise data sources.
• Optimize prompt engineering strategies, system instructions, and grounding mechanisms for production-grade LLM deployments.
• Develop end-to-end ML pipelines from data ingestion and feature engineering through model training, evaluation, and production deployment on Vertex AI Pipelines / Kubeflow.
• Apply advanced data science techniques — statistical modelling, time-series forecasting, recommendation systems, and anomaly detection — for hospitality and gaming analytics.
• Build scalable data transformation and feature engineering workflows using BigQuery, Dataflow, and Pub/Sub.
• Implement model monitoring, drift detection, and automated retraining strategies to ensure sustained model performance in production.
• Leverage TensorFlow, JAX, or PyTorch for custom model development where pre-trained solutions are insufficient.
• Architect and manage cloud-native AI infrastructure on GCP — including Vertex AI, BigQuery ML, Cloud Run, GKE, Cloud Functions, and Cloud Storage.
• Design secure, scalable, and cost-optimized GCP environments aligned with enterprise compliance requirements and CLIENT's data governance standards.
• Implement CI/CD pipelines for ML model serving using Cloud Build, Artifact Registry, and Vertex AI Model Registry.
• Set up monitoring, observability, and alerting for AI/ML workloads using Cloud Monitoring, Cloud Logging, and custom dashboards in Looker.
• Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex.
• Design and implement conversational AI agents using Dialogflow CX and Agent Builder for guest-facing and internal automation use cases.
• Partner with CLIENT's business and technology stakeholders to define AI use cases, prioritize the roadmap, and translate requirements into technical deliverables.
• Champion responsible AI practices — model fairness, explainability, content safety, and data privacy — across all AI solution designs.
• Produce and maintain technical documentation including architecture decision records (ADRs), API specs, model cards, and runbooks.
• Mentor junior engineers and lead knowledge-sharing sessions; contribute to AI community of practice within the delivery organization.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field.
• Hands-on engineering experience with at least 3+ years focused on AI/ML or LLM development on cloud platforms.
• Strong proficiency in Python; working knowledge of SQL; familiarity with infrastructure-as-code tools (Terraform, Cloud Deployment Manager) preferred.
• Google Cloud Professional certifications (Cloud Architect, ML Engineer, Data Engineer)

Tech Stack

• BigQuery
• Cloud
• Flash
• Google Cloud Platform
• Python
• PyTorch
• SQL
• Tensorflow
• Terraform

Benefits

• 100% payroll + benefits by law
• Work from home (Anywhere in México)
• 30-day Christmas bonus
• 16 vacation days
• 1 economic day a year
• Grocery coupons
• Savings fund
• Home office stipend
• Major medical insurance
• Life insurance
• Training & certifications