• Diseñar y construir sistemas de agentes de IA capaces de ejecutar flujos de trabajo autónomos (agentic workflows), orquestando múltiples modelos, herramientas y fuentes de datos para resolver problemas complejos de negocio. • Desarrollar con programación asistida por IA (Claude Code, Cursor, Copilot…), maximizando tu productividad y la del equipo. • Implementar soluciones de IA Generativa en producción : sistemas RAG avanzados, chatbots inteligentes, pipelines de procesamiento de lenguaje natural, generación de contenido y automatizaciones basadas en LLMs. • Integrar la IA en el stack de producto , conectando soluciones inteligentes con el frontend, backend, APIs y plataformas de datos existentes de nuestros clientes. • Experimentar y prototipar rápido : alineado con nuestra filosofía de iteración de alta frecuencia, probarás nuevos modelos, técnicas y arquitecturas para validar hipótesis de crecimiento con IA. • Contribuir a la estrategia de IA de Product Hackers , identificando oportunidades para aplicar inteligencia artificial al Growth de productos digitales y proponiendo nuevas líneas de servicio.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are growing the Financial Assistant team at Plata, building intelligent systems that support users in managing their finances, understanding spending, and interacting with financial products in a simple and intuitive way. This team plays a key role in improving customer experience, engagement, and the overall value of our core products. This is a customer-facing product in a regulated environment — accuracy, safety, and trust are non-negotiable. We use AWS, Go, Python and cloud-based models, yet still flexible to mix ready-made tools and ship our custom solutions. We're all about building systems that deliver real, valuable results for our organization. You will be a key specialist in a cross-functional team, working closely with backend, mobile and other LLM engineers. Challenges that await you: - Work with agentic architectures where needed: tool use, multi-step reasoning, orchestration, and failure recovery - Design and maintain eval infrastructure (offline test suites, golden datasets, regression harnesses) that gives a reliable signal after every change - Work with advanced prompts, set up RAG-oriented data sources for efficient retrieval, and evaluate model outputs to ensure accuracy, relevance, and quality - Implement safety and guardrails: hallucination detection, refusal strategies, factual grounding - Run online and offline experiments (canary, A/B) to validate that changes improve real user outcomes - Own observability from day one: trace LLM calls, monitor quality drift, latency, and cost per session in production - Collaborate with backend engineers and product to ship reliable improvements, review code, and maintain production health Qualifications - Strong classical ML fundamentals, you understand what's happening inside models, not just how to call their APIs - Strong eval mindset: you design the measurement system before writing the first prompt, and treat evals as a first-class engineering artifact - Hands-on experience with search and retrieval: dense/sparse/hybrid, reranking, query understanding — using managed tooling effectively, not necessarily from scratch - Practical experience with agentic architectures: tool use, orchestration, failure recovery - Safety-first thinking: guardrails, content policies, graceful degradation under uncertainty, especially in a context where wrong answers have real consequences - Production ML ownership: observability, latency budgets, cost tracking, regression detection - Expertise in Python and its ecosystem, as well as language- and framework-agnostic mindset to achieve project goals - Familiarity with open-source tools to build and evaluate RAG applications - Excellent communication skills and ability to explain complex technical concepts to a broad audience of stakeholders - B1 or higher English level for effective communication with an international team Requirements - Previous experience delivering business-critical ML/AI-powered applications for customer-facing products - Fine-tuning or distillation of LLMs for production use cases - Experience deploying open-source models (Llama, Mistral) in private/on-premise environments - Experience in fintech, banking, or regulated domains

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers an exciting opportunity to lead the development of next-generation AI systems at scale, transforming how millions of users interact with intelligent financial services. As Lead Software Engineer, you will: - Define and implement AI architectures. - Orchestrate agentic workflows. - Integrate LLMs and Generative AI into production-grade systems. - Collaborate closely with product, design, and data teams to ensure AI features deliver safe, personalized, and high-impact experiences. - Provide technical leadership and hands-on engineering. - Shape AI standards, mentor teams, and influence strategic decisions across multiple markets. - Work remotely from the U.S., with occasional travel to build relationships and align on initiatives. Accountabilities: - Lead the AI architecture and technical roadmap, ensuring LLM integrations are scalable, reliable, and cost-effective. - Design and implement agentic workflows and retrieval-augmented generation (RAG) patterns that enhance customer experiences. - Establish and promote engineering standards for AI development, creating reusable components and best practices. - Collaborate across teams, including Product, Design, and Data Science, to embed AI capabilities across web, mobile, and internal tools. - Define and monitor quality, safety, and evaluation practices for AI outputs, ensuring human-in-the-loop integrity. - Mentor engineers and participate in hiring, fostering high performance and knowledge sharing within technical guilds and AI learning programs. - Balance short-term delivery with platform stability, making informed strategic trade-offs on model selection, system complexity, and operational efficiency. Qualifications - Proven experience as a technical leader delivering large-scale software systems in production environments. - Hands-on experience implementing LLMs, Generative AI, or RAG workflows at scale. - Proficiency in full-stack development, including maintainable codebases and API design bridging AI inference layers with user-facing surfaces. - Strong problem-solving skills, with experience managing complex, ambiguous projects across multiple teams and stakeholders. - Strategic mindset linking technical AI initiatives to business outcomes such as efficiency, customer acquisition, and product innovation. - Experience mentoring and scaling engineering teams, fostering collaboration and high performance. - Bonus: familiarity with PyTorch, TensorFlow, Python AI toolchains, cloud-native infrastructure (AWS/Azure), mobile frameworks like Flutter, and regulated industry standards (FinTech, Responsible AI). - Experience in global-scale products serving diverse markets and multiple languages is a plus. Requirements - Proven experience as a technical leader delivering large-scale software systems in production environments. - Hands-on experience implementing LLMs, Generative AI, or RAG workflows at scale. - Proficiency in full-stack development, including maintainable codebases and API design bridging AI inference layers with user-facing surfaces. - Strong problem-solving skills, with experience managing complex, ambiguous projects across multiple teams and stakeholders. - Strategic mindset linking technical AI initiatives to business outcomes such as efficiency, customer acquisition, and product innovation. - Experience mentoring and scaling engineering teams, fostering collaboration and high performance. - Bonus: familiarity with PyTorch, TensorFlow, Python AI toolchains, cloud-native infrastructure (AWS/Azure), mobile frameworks like Flutter, and regulated industry standards (FinTech, Responsible AI). - Experience in global-scale products serving diverse markets and multiple languages is a plus. Benefits - Remote-first work with quarterly travel to collaborate with colleagues in strategic hubs. - Top-tier medical, dental, and vision insurance. - Generous paid time off, company holidays, and supportive work-life balance culture. - Life Insurance and AD&D coverage. - Extended maternity and paternity leave. - Access to internal learning programs and language courses for professional development. - Mental health and wellness support programs. - Retirement and savings plans including 401K, HSA, and FSA options.

• Lead engineering teams that deliver AI-powered product and platform capabilities with clear impact. Reduce administrative toil, improve family responsiveness, streamline enrollment, and surface better insights for operators. • Build software that not only records information, but also recommends next steps, completes routine work, and automates meaningful parts of our customers’ day-to-day operations. • Use AI agents, automation, and modern tooling to improve how teams build, test, ship, and support software, increasing velocity and freeing up more capacity for high-value work. • Operate an AI hybrid engineering workforce in which engineers and agents work together effectively, with the tooling, guardrails, governance, evaluation, and observability required. • Apply the same AI-native mindset to internal functions like customer support and onboarding, helping the company become faster, more responsive, and more efficient as it scales. • Drive continuous improvements in quality, reliability, and security by using agentic workflows to catch issues earlier, reduce operational burden, and improve the product every week. • Hire and scale exceptional AI-native engineering talent, building a lean, high-performing organization with high standards, strong ownership, and a bias for action. • Make strong build-versus-buy, architecture, and platform decisions that balance short-term speed with long-term leverage and differentiation.