AI Jobs in Argentina

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Debug and fix issues in the platform and ship pull requests with those fixes. Build internal tools and copilots powered by generative AI to enhance the team’s capabilities. Rapidly prototype proof-of-concepts for customer use cases. Work collaboratively across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Work on projects such as developing a product that root causes KTLO work and recommends solutions, building a software catalog that works for monoliths and is user-friendly, and helping protect engineering focus time by systemically solving sources of distraction or mental load with AI.

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Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

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Own end-to-end implementation of AI-driven detection features, from discovery to production deployment and iteration. Design and build data enrichment pipelines to extract structured information from messy, real-world financial transaction data. Research fraud and scam typologies relevant to older adults and translate findings into scalable detection logic. Build evaluation frameworks including metrics, error analysis, and model comparisons to measure system performance and drive improvement. Optimize AI pipelines for accuracy, latency, and cost by making informed tradeoffs on model selection and architecture. Collaborate with Customer Service, Go-to-Market, and partner-facing teams to ensure solutions meet real-world needs and deliver measurable impact. Stay current with developments in LLMs, agent architectures, and applied AI to identify practical applications for the domain.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

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Debug and fix issues in the Arize platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team's productivity. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concept solutions for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and push the boundaries of AI adoption.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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Contributors design original computational statistics problems that simulate real mathematical research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications from mathematical practice; verify solutions using Python with standard mathematical libraries; and document problem statements clearly while providing verified correct answers.

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The Salesforce Technical Architect is responsible for designing and delivering scalable, enterprise-grade cloud solutions across the Salesforce ecosystem, including Sales Cloud, Service Cloud, Experience Cloud, Data Cloud, and Agentforce AI agents. The role includes architecting AI-enabled workflows and autonomous agents, designing enterprise data strategies leveraging Salesforce Data Cloud, and defining scalable enterprise integration architectures using MuleSoft or other iPaaS platforms. The architect provides technical leadership for development teams working with Apex, Lightning Web Components, Visualforce, and Salesforce automation frameworks, and may contribute hands-on when necessary. They ensure solutions are high performance, scalable, secure, and capable of handling large data volumes. The role involves partnering with Delivery Managers, consultants, and client stakeholders to translate business requirements into technical architectures, leading design and code reviews, and mentoring developers and consultants. Additionally, the architect evaluates and implements Salesforce AI capabilities, drives innovation initiatives, serves as a technical advisor to clients on architecture and AI adoption, contributes to internal architecture standards and reusable frameworks, and supports presales and solutioning efforts for complex Salesforce engagements.