Neon

• Define and evolve the technical vision and engineering strategy across multiple domains, ensuring alignment with overall business objectives. • Lead, mentor, and develop engineering managers and senior technical leaders, fostering a culture of continuous learning and high performance. • Ensure predictable, high-quality delivery of complex products by managing technical roadmaps and execution risks. • Promote operational excellence through robust SRE practices, observability, and automation across the software lifecycle. • Act as a strategic partner to Product, Data, Risk, and Compliance teams to enable financial innovation in a regulated environment. • Manage the department budget, headcount planning, and strategies for attracting and retaining top technical talent. • Influence the architecture of large-scale distributed systems, ensuring resilience, security, and cost efficiency.

• Develop and architect AI agent systems based on single-agent and multi-agent architectures. • Implement modern communication protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A). • Master the information lifecycle for AI: from strategies like chunking and embeddings to implementing techniques such as RAG (Retrieval-Augmented Generation), reranking, and query transformation. • Manage the complexity of multi-model environments. Evaluate and select the best providers based on trade-offs in latency, context, and cost. Standardize the use of abstraction frameworks to avoid vendor lock-in. • Act as a technical mediator in defining AI strategies across the product portfolio. • Ensure architectural consistency across initiatives, identifying opportunities for componentization and reuse. • Serve as a decision-maker in architectural dilemmas, promoting an AI Engineering culture. • Connect AI technical capabilities to the real pain points of business areas. Establish LLMOps pipelines that include: • Rapid experimentation cycles (A/B testing of prompts/models) • Feedback systems (human-in-the-loop) • Monitoring of business metrics impacted by AI • Systematic evaluation (Evals) - creation of test datasets and frameworks.

• Define and evolve the architecture of critical systems, ensuring scalability, resilience, and efficiency • Lead complex technical initiatives that span multiple teams • Make technical decisions with long-term vision and business impact • Serve as a reference for design, trade-offs, and engineering quality • Influence the company’s technical direction, preventing fragmentation of solutions • Connect technology with business metrics and tangible outcomes • Unblock complex problems (technical and organizational) • Mentor senior engineers and staff, elevating the organization’s technical level • Collaborate with product, data, and stakeholders to define strategies • Promote best practices, standards, and technical consistency across teams

• Value creation: Build proofs of concept (POCs), internal tools, and abstractions that realize the potential of AI and solve company problems. • Technical enablement: Configure, sustain, and guide best practices for the AI tools ecosystem (such as Claude Code, Cursor, Copilot, n8n, etc.), ensuring appropriate technical support. • Knowledge multiplication (Evangelism): Create training programs and playbooks and engage actively (including 1:1 sessions) to teach people, remove adoption barriers, and drive usage across the company. • Intelligence and impact measurement: Define and structure AI adoption and success metrics (dashboards), helping teams understand the value generated (e.g., throughput gains, hours saved).

• We strive to be an increasingly exceptional team—building, supporting, and recognizing diverse teams that continually raise the bar! • We are a fintech founded by a Brazilian who, dissatisfied with traditional banks, set out to build a service that makes people’s financial lives simpler and less bureaucratic. • Our goal is to simplify the banking system; we are driven by a strong purpose: creating pathways to a better financial life for all Brazilians. • We leverage technology to innovate and develop the best solutions for people working hard to improve their lives.

• Conduct studies and develop credit policies; • Study and monitor the various strategies applied; • Monitor, analyze and forecast portfolio and loan vintage indicators; • Analyze and project profitability; • Continuously seek opportunities.

• Internal Communication: Design and manage the internal communication ecosystem. We want clear, authentic messages that reach everyone, respecting the diversity of our team. • Engagement Rituals: Facilitate our moments of gathering (All Hands, events, celebrations) and ensure our rituals reinforce who we are and where we’re headed. • Leadership Partnership: Act as a business partner to leadership on critical communications, change management and crisis response, always prioritizing transparency and active listening. • Data-driven Culture: Manage eNPS and engagement surveys, turning numbers into actionable plans that improve the end-to-end Neon experience. • Innovation and AI: Explore Artificial Intelligence tools and new technologies to scale our culture, personalize communication, and make day-to-day work more fluid and intelligent.

• Your job is to go sell it to them. • You will own the full customer journey from initial awareness and licensing to customer success and follow-on expansion. • Your analytical nature means you bias toward winning business by proving exactly how a client's model performance and business objectives benefit from partnering with Neon. • You will also lead cross-functional efforts, acting as the bridge between our customers and our product and engineering teams to align what we build with what you sell.

• Own end-to-end experimentation and analytical workflows that help the business learn fast and make confident decisions rooted in data. • Work closely with product, data engineering, and business stakeholders to start with customer and business goals, and work backwards to design the right experiments, features, metrics, and models. • Focus initially on optimizing our data pipeline for media data (audio and text) — helping improve throughput, quality, and utility of data as it flows through our systems. • Contribute to the evolution of our data infrastructure to support more advanced capabilities — including eventual creation of novel datasets and internal modeling workflows (e.g., for voice or multimodal data). • Design, analyze, and interpret experiments across tools/services/technologies to identify what works best — producing actionable insights that influence product direction, operations, and future research. • Translate complex analytical outcomes into clear business insights and product recommendations, occasionally contributing to broader thought leadership and research narratives.

• Design, build, and maintain scalable, robust, and automatable data pipelines to support ingestion and processing across structured, unstructured, and media data types (including audio, text, and video). • Partner with product, data science, and business stakeholders to translate business goals and customer needs into engineering requirements — starting with the desired outcome and working backwards into concrete, scalable solutions. • Improve data quality, observability, lineage, and reliability across our modular processing pipeline architecture. • Build and maintain core data infrastructure components (e.g., ingestion APIs, metadata services, observability tooling) that support parallel processing, microservices, and config-driven workflows. • Help evolve our approach to handling time-series and media content at scale, optimizing for throughput, cost efficiency, and real-world performance. • Collaborate across teams to validate production data flows, support onboarding of new datasets, and troubleshoot issues quickly and effectively.