AI Jobs in Argentina

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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As a Senior Full Stack Engineer at Siena, you will own meaningful parts of the platform end to end, taking ambiguous problems and working out the right approach to ship reliable, high-performance systems that real brands depend on every day. You will own features and systems across the full stack including frontend, backend, and infrastructure from problem definition through production. You will partner with product to break down ambiguous scope and ship in iterative, high-impact releases, make real architectural and design decisions in your area and explain the reasoning to the team, and integrate cutting-edge language models into enterprise customer workflows where reliability and safety are critical. You will solve hard engineering problems including API performance, microservices, and scaling across channels and brands. Additionally, you will maintain and improve AWS infrastructure with a DevOps mindset and contribute to raising the bar around you by sharing knowledge, reviewing thoughtfully, and helping teammates level up.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.

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As a Forward Deployed Engineer at Wonderful, you will solve high-impact, real-world problems alongside enterprise customers and own work from first principles through production outcomes. You will work directly with customers to understand critical workflows, design AI-driven solutions, and deploy agents that operate reliably in production. Responsibilities include partnering with customers to understand operational challenges and success criteria, translating open-ended problems into clear technical designs and implementations, building AI agents that integrate with customer systems and perform real tasks, owning deployments including reliability, performance, and continuous improvement, testing with real data, measuring outcomes, and iterating based on results. You will collaborate with Product & Engineering to ship high-quality solutions and contribute to tools, documentation, and best practices that scale customer delivery.

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The opportunity involves generating prompts that challenge AI, evaluating AI-generated solutions for correctness, assumptions, and logic, improving AI reasoning to align with first principles and accepted standards, and applying structured scoring criteria to assess multi-step problem solving. Contributors participate in project-based AI opportunities, focusing on testing, evaluating, and improving AI systems through part-time, non-permanent projects.

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Own configuration, administration, and ongoing maintenance of our AI-powered application platform. Collaborate with infrastructure engineers to shape the framework and standards that govern how AI-assisted applications are built, reviewed, and deployed. Design and build tooling that enables teams to develop and iterate on applications with greater independence and less engineering overhead. Evaluate AI-generated code for security, scalability, and production readiness, and define code review standards specific to AI-assisted development workflows. Serve as a technical advisor to non-technical stakeholders — helping them understand what is possible, where risk lives, and when engineering involvement is required. Establish and promote best practices for AI-assisted development across the engineering organization. Partner with the AI Solutions team and engineering leadership to define the long-term architecture of this platform as it scales.

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Design, build, and ship full stack features across the Fuse lending platform including backend APIs and services and customer-facing dashboards and operator tools. Model and configure each client's lending domain including loan types, underwriting rules, debt adjustment rules, task flows, adverse actions, and email/e-sign templates. Build and maintain client-specific automations and workflows that orchestrate the end-to-end loan lifecycle integrating with external services such as credit bureaus, KYC providers, banking rails, and document services. Ship AI-powered features such as LLM-based automations, classification and extraction pipelines, agentic workflows, and tool-using agents that operate on real customer data. Own features end-to-end including scoping, implementation, testing, deployment, monitoring, and iteration based on production signals. Write clean, well-tested, observable code and contribute to shared standards on code quality, reliability, and security.

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Diseñar, construir y operar servicios core de la plataforma que soportan los flujos de AI/LLM. Desarrollar y correr sistemas basados en LLM, como text-to-SQL, reasoning agents y planners, enfocándose en confiabilidad, performance y costos. Construir y mantener servicios backend y APIs que orquestan pipelines complejos y multi-step. Trabajar hands-on con infraestructura basada en Kubernetes, incluyendo despliegues, escalado y debugging en producción. Contribuir a workflows de DevOps y MLOps, incluyendo CI/CD, ambientes, monitoreo, logging y buenas prácticas operativas. Participar en decisiones de arquitectura relacionadas con observabilidad, manejo de fallas, evaluación y trade-offs del sistema. Colaborar directamente con los fundadores y el equipo de producto para transformar problemas reales de negocio en soluciones técnicas robustas.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.