AI Engineer

• Design, build, and operate AI agents that perform multi-step reasoning, decision-making, and task execution across enterprise systems.
• Develop agent-based solutions using LLMs, tools/function calling, memory, and orchestration frameworks to automate end-to-end business processes.
• Implement human-in-the-loop, approval, audit, and exception-handling patterns to ensure safe and reliable automation.
• Define and track KPIs for agent effectiveness (e.g., cycle-time reduction, automation rate, error rate, cost savings) and continuously optimize performance.
• Design and maintain AI-driven workflows that integrate with enterprise applications (CRM, ERP, ticketing, content management, collaboration platforms).
• Build and expose services and APIs that allow internal applications to invoke AI agents and automation capabilities.
• Integrate and govern third-party AI platforms and services where they accelerate delivery and reduce operational risk.
• Ensure reliability, observability, and cost controls across automated processes.
• Develop and deploy LLM-powered capabilities including tool-using agents, prompt strategies, embeddings, vector search, and retrieval-augmented generation (RAG) where appropriate.
• Evaluate model and agent behavior using quantitative metrics, structured test cases, and regression suites.
• Apply guardrails, validation, and monitoring to ensure responsible, predictable, and secure AI behavior in production.
• Operate AI agents and automation solutions in production, responding to incidents, failures, and unexpected behaviors.
• Partner with IT Operations and Security to ensure solutions meet availability, security, privacy, and compliance requirements.
• Participate in on-call or escalation rotations as required for critical AI-enabled systems.
• Partner with business stakeholders to identify high-impact automation opportunities and translate them into AI agent designs.
• Provide technical leadership, design guidance, and mentorship to engineers working on AI and automation initiatives.
• Contribute to architectural standards, best practices, and documentation for agent-based AI systems.

Senior AI Engineer

• Design and build AI-powered features, pipelines, and automation workflows from scratch
• Integrate and fine-tune LLMs, embedding models, and other ML systems into production applications
• Develop and maintain RAG pipelines, vector search systems, and agent-based architectures
• Write clean, well-structured code across backend and API layers to support AI feature delivery
• Evaluate, benchmark, and iterate on model outputs to ensure quality and reliability
• Collaborate with cross-functional teams to scope requirements and architect AI solutions
• Stay current with the rapidly evolving AI landscape and proactively introduce relevant tooling and approaches
• Document technical designs, system behaviour, and deployment processes clearly and thoroughly

• Diseñar y desarrollar copilotos de IA para Dev/QA y soluciones RAG sobre repositorios .
• Construir agentes de automatización para procesos como post‑sales, claims, deliveries y suppliers.
• Implementar controles de seguridad y gobernanza de IA (permisos, costos, evaluación y trazabilidad).
• Crear bases de conocimiento, RAG y estrategias de despliegue .
• Medir impacto en negocio a través de KPIs como TTR, SLA y reducción de retrabajo.

Gen AI Engineer

• Lead the architecture, development, training, optimization, and deployment of AI/ML models and pipelines from prototype to production scale.
• Develop cloud-based AI services (leveraging LLMs and multimodal models via APIs like Gemini, OpenAI, or equivalents) for advanced personalization, recommendation engines, natural language understanding of travel queries, and feedback loops.
• Establish and enforce best practices: clean ML code, experiment tracking, MLOps (model versioning, monitoring, A/B testing, drift detection), automated evaluation pipelines, cost-efficient inference, and ethical AI considerations (bias mitigation, privacy).
• Collaborate on feature definition, data requirements, success metrics, and iteration.
• Handle unique challenges in an AI-powered travel app: multimodal data (location, text, images), sparse/seasonal travel datasets, real-time adaptability, offline/online hybrid inference, and seamless integration with mobile clients and backend APIs.
• Mentor future team members as we expand the AI engineering group and help shape our AI culture from the start.

Senior ML/AI Engineer

• Build and deliver AI-powered features based on functional and technical requirements provided by TPMs and Principal AI Engineers
• Develop end-to-end AI solutions, including backend services, APIs, and application-layer integrations
• Integrate large language models (LLMs) into production systems, ensuring performance, scalability, and reliability
• Design, test, and optimize prompts to improve output quality and consistency
• Collaborate with ML Engineers to embed models trained or fine-tuned on proprietary data into applications
• Participate in model evaluation and validation, ensuring outputs meet quality, accuracy, and performance benchmarks
• Support testing and deployment of AI features through CI/CD pipelines
• Troubleshoot and refine AI behaviours in real-world production scenarios.

• Diseñar, entrenar y optimizar modelos de Machine Learning y Deep Learning para resolver necesidades específicas de negocio.
• Implementar técnicas de fine-tuning y optimización de modelos generativos y LLMs.
• Desarrollar soluciones basadas en Retrieval-Augmented Generation (RAG) combinando recuperación de información con generación de contenido.
• Integrar modelos de IA en aplicaciones mediante APIs RESTful utilizando FastAPI.
• Diseñar e implementar estrategias de búsqueda semántica utilizando bases de datos vectoriales.
• Gestionar y optimizar vector databases para consultas de alto rendimiento.
• Participar en el diseño técnico, revisión de código y evolución de las soluciones de IA.

AI Engineer, Ssr

• Develop and implement AI solutions using frameworks such as LangChain, LangGraph, and multi-agent systems.
• Utilize prompt engineering and retrieval-augmented generation (RAG) to enhance AI capabilities.
• Work with vector databases (ChromaDB, Pinecone) and implement context optimization and model routing.
• Ensure LLM observability and adherence to AI governance standards.
• Lead Agile teams through project lifecycles from conception to deployment, ensuring best practices in software development and architecture.
• Manage CI/CD processes, including logging, tracing, and monitoring for infrastructure observability.
• Conduct code reviews and enforce design patterns to maintain high-quality code standards.
• Collaborate effectively with stakeholders to align on project goals and deliverables.

AI Architect, AWS

• Apoyar el desarrollo de soluciones basadas en IA generativa utilizando Azure OpenAI Services
• Diseñar e implementar asistentes, agentes y flujos automatizados
• Participar en el entrenamiento, evaluación y despliegue de modelos de Machine Learning
• Ejecutar análisis exploratorio de datos (EDA) y construcción de modelos descriptivos
• Elaborar y documentar pipelines de procesamiento y modelos

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