AI Engineer

• Design module content for the FinTech Engineering Specialization at Jala University
• Develop labs
• Create a reference implementation
• Build an evaluation harness
• Prepare an instructor guide

Academic Content Developer – AI, Fraud Detection, Financial Systems

• Design and engineer robust machine learning features on Databricks to power AI models and solve complex business challenges
• Implement features with complete standard metadata, including description, keys, lineage, ownership, tags, and data lifecycle
• Apply scalable design patterns to maximize organizational reuse
• Follow deployment pipelines including CI/CD, bundle validation, and PRD
• Oversee job monitoring and apply FinOps practices for efficient cloud resource usage within Databricks
• Ensure consistency between training and production environments
• Maintain a single source of truth for all machine learning models
• Work closely with cross-functional teams to create trustworthy, well-governed AI products

AI Engineer, Databricks

• Design, develop, and maintain mobile applications for iOS and Android using React and Capacitor
• Build scalable, maintainable, and high-performance user interfaces using React 18 and TypeScript
• Develop and maintain native mobile integrations and Capacitor plugins as needed
• Configure and manage build environments using Vite
• Collaborate with backend, product, and design teams to deliver seamless user experiences
• Troubleshoot, optimize, and enhance application performance across mobile platforms
• Manage mobile application releases, updates, and deployments to the Apple App Store and Google Play Store
• Participate in code reviews and contribute to engineering best practices

Senior Mobile Engineer, AI-Assisted

• Build and maintain Unity (C#) field applications for sketching, configuring, and publishing pipe, duct, and other MEP work from jobsites
• Write production Unity code covering scene and prefab architecture, component lifecycle, input handling, UI, performance, and editor/runtime separation
• Own client-to-cloud integration, login, publishing, data synchronization, fidelity, and error handling between the Unity client and Stratus services
• Build and maintain automated Unity tests using NUnit, PlayMode integration tests, and EditMode unit tests
• Ship customer-facing enhancements across backend services, APIs, data, and web front end
• Design and build services and APIs connecting the web platform and cloud data
• Investigate and fix customer-reported bugs across client, service, and data layers
• Implement unit, integration, and end-to-end automated testing
• Contribute to monolith decomposition and modernization
• Use AI-assisted development tools and validate AI-generated changes with tests and engineering judgment
• Collaborate with product managers, designers, and customer-facing teams to define, design, scope, and ship features
• Coordinate deployments and incident response
• Advocate for incremental delivery
• Share knowledge through code reviews and pragmatic best practices

Unity Developer – AI Forward

• Build and operate production AI systems improving Hyatt’s search, personalization, guest experiences, colleague productivity, and operational workflows
• Design scalable AI systems for natural-language search, semantic retrieval, ranking, recommendations, and personalized guest experiences
• Develop low-latency retrieval and ranking pipelines using keyword search, embeddings, vector retrieval, reranking, business rules, and real-time contextual signals
• Partner with data scientists to productionize relevance models, recommender systems, and LLM-powered search experiences
• Define experimentation approaches, relevance metrics, latency targets, A/B tests, and business-impact measurement
• Lead high-throughput, low-latency LLM inference services
• Optimize model serving through model selection, quantization, batching, caching, streaming, routing, autoscaling, and efficient GPU utilization
• Build production APIs and services for inference, embeddings, reranking, retrieval, and agent execution
• Deploy systems using AWS-native services and modern containerized infrastructure
• Architect and implement agentic AI and multi-agent applications using LLMs, tools, retrieval, workflows, memory, and structured decision-making
• Design orchestration patterns for planning, tool execution, state management, retries, handoffs, approvals, and fallback behavior
• Establish secure agent interfaces with identity, authorization, isolation, traceability, and policy enforcement
• Develop reusable agent platform components, registries, workflow templates, evaluation harnesses, and observability standards
• Establish CI/CD, testing, versioning, infrastructure-as-code, reproducibility, and rollback practices
• Implement observability for models and agents, including latency, availability, cost, quality, retrieval health, failures, drift, safety events, and business outcomes
• Create evaluation frameworks covering benchmarks, human evaluation, adversarial testing, guardrails, bias checks, hallucination analysis, and production monitoring
• Work with security, privacy, legal, architecture, and governance stakeholders to ensure safe, compliant, reliable, and responsible AI deployment
• Support batch and real-time inference across the model lifecycle
• Serve as hands-on technical lead for AI initiatives from discovery through production operation
• Translate business opportunities into engineering problem statements, architectures, delivery plans, success metrics, and technical tradeoffs
• Influence AI and ML platform roadmaps
• Mentor engineers and data scientists through design reviews, code reviews, architecture discussions, and engineering best practices
• Communicate technical decisions, model limitations, risks, and measured outcomes to technical and business stakeholders

Lead AI Engineer – Search, Personalization, Agents

• Design, build, and deploy production-grade data pipelines, analytics engines, and Generative AI/LLM applications using Databricks, Spark, Python/PySpark, and modern cloud ecosystems
• Serve as the primary technical leader on strategic enterprise client engagements
• Partner with C-level executives, VP/Directors of Data, and engineering teams to translate business challenges into technical solutions
• Lead complex legacy platform migrations onto the Databricks Lakehouse architecture
• Guide enterprise customers in adopting GenAI solutions, RAG architectures, model fine-tuning, and MLflow/Databricks Mosaic AI integrations
• Identify high-value technical use cases across client business units, expand Databricks platform usage, and drive measurable client ROI
• Mentor junior Solution Engineers, Architects, and Consultants
• Contribute to internal RevStar code libraries, reusable accelerators, and technical delivery best practices

Forward Deployed Engineer, AI/ML

• Create realistic, challenging coding tasks involving feature implementation and software bug fixes.
• Design tasks that reflect real-world backend engineering scenarios.
• Develop deterministic verifiers that accept reasonable valid solutions and reject incorrect outputs.
• Work with backend technologies such as Java, Node.js, Go, Rust, TypeScript, Python, C++, or similar languages.
• Evaluate code for correctness, reliability, maintainability, and performance.
• Incorporate automated testing and clear validation criteria into coding tasks.
• Identify and account for edge cases, performance bottlenecks, and potential implementation errors.
• Apply secure coding principles and cybersecurity considerations where relevant.
• Refine tasks and verification logic based on project requirements and quality standards.

Backend Software Engineer – AI Training Projects

• Traducir retos de negocio en modelos y arquitecturas basadas en Machine Learning, Deep Learning, LLMs, RAG y Agentes
• Diseñar pipelines completas de GenAI y RAG, incluyendo ingesta, embeddings, recuperación, reranking, evaluación y monitoreo continuo
• Construir agentes inteligentes con herramientas, gestión de memoria y estado, permisos, protocolos de integración y mecanismos Human-in-the-loop
• Garantizar el ciclo de vida completo, despliegue continuo y trazabilidad de modelos mediante prácticas MLOps, LLMOps y AgentOps
• Aplicar caching sistemático, destilación de modelos y técnicas de inferencia eficiente para optimizar costos y rendimiento
• Implementar controles de seguridad, ética, privacidad y límites de autonomía para operar soluciones técnicas en producción
• Habilitar capacidades de negocio, automatizar procesos complejos y elevar la experiencia digital mediante soluciones de IA integradas a la arquitectura y políticas de Openpay

• Design, build, maintain, and optimize scalable production data-transformation pipelines
• Develop and maintain centralized master tables and semantic layers as the business's single source of truth
• Design and implement agentic AI solutions, intelligent assistants, RAG systems, and prompt engineering using technologies such as Amazon Bedrock, Gemini, and ChatGPT/Enterprise
• Optimize heavy data queries and transformations, and improve the efficiency and cost of LLM token consumption
• Translate business and operational needs into analytical and AI assets that automate tasks, improve productivity, and create new Embedded Finance capabilities
• Build robust, scalable, and unified data products for Embedded Finance
• Convert AI and foundation models into practical, integrated solutions that automate operational tasks and improve productivity
• Lead the full data and AI solution lifecycle from pipeline development through production deployment and monitoring

Data & AI Engineer Expert – Embedded Finance

• Desarrollar e implementar agentes de IA y automatizaciones
• Ejecutar las estrategias definidas por el Head of AI
• Trabajar con LLMs, RAG y herramientas de IA en proyectos reales
• Integrar soluciones de IA con plataformas y bases de datos
• Experimentar, probar y mejorar continuamente modelos y flujos de IA
• Trabajar con equipos internacionales para convertir el interés en clientes reales

AI Engineer jobs in Mexico