Forward Deployed Engineer AI - Sênior

Accountabilities:: Analyze business processes, performance indicators, and operational challenges to identify opportunities where AI agents can deliver measurable value, while recognizing situations where alternative solutions are more appropriate. Lead product discovery with business stakeholders, defining the problem, target users, desired outcomes, priorities, and success criteria before selecting the appropriate technical approach. Collaborate with business teams to design AI agents, specifying their responsibilities, decision-making capabilities, data and system access, and the appropriate points for human intervention. Build and deploy initial end-to-end AI agent use cases on the existing platform, implementing the necessary integrations, tools, and Model Context Protocol (MCP) servers. Enable non-technical teams to independently create, use, and improve AI agents through practical workshops, concise learning materials, hands-on guidance, and ongoing support. Translate real-world implementation insights into actionable product feedback, communicating platform gaps, recurring challenges, and improvement opportunities to product managers with concrete examples. Monitor agents in production from both business and technical perspectives, tracking adoption, process improvements, response quality, operational effectiveness, and costs. Partner with AI engineers and software developers when use cases require deeper technical expertise, more complex integrations, or custom engineering solutions. Establish reusable practices and implementation approaches that support broader AI adoption across multiple business units and organizations. Requirements: Business and product mindset: Ability to quickly understand business operations, engage confidently with non-technical managers, identify meaningful problems, prioritize initiatives based on impact, and challenge use cases that do not justify an AI agent. Communication and enablement: Strong communication and stakeholder management skills, with the ability to explain complex technical concepts without jargon, influence decision-making, and teach others effectively. AI training and adoption: Previous experience leading AI-focused training, enablement, or evangelization initiatives, particularly for business teams and non-technical users. Applied AI experience: At least 2 years of hands-on experience implementing AI solutions in business environments and bringing Large Language Model (LLM)-based applications into real-world production use. AI agent development: Proven experience designing and building AI agents using platforms or frameworks such as LangChain, LangGraph, Vercel AI SDK, OpenAI Agents SDK, Claude Agent SDK, or equivalent technologies. LLM techniques and evaluation: Strong understanding of prompt engineering, tool calling, Retrieval-Augmented Generation (RAG), and response evaluation, including the ability to identify agent limitations and design reliable solutions around them. MCP and integrations: Experience building or consuming Model Context Protocol (MCP) servers to connect AI agents with enterprise data, tools, and systems. Software engineering: At least 5 years of software development experience, with demonstrated experience delivering and maintaining production code. TypeScript and Node.js: Strong proficiency in TypeScript/Node.js, with the ability to independently develop, debug, and maintain integrations and applications. APIs and system architecture: Practical knowledge of REST APIs, authentication mechanisms, and system-to-system integrations. Autonomy and adaptability: Comfortable navigating ambiguity, working across business functions, asking the right questions, and taking ownership of initiatives from discovery through implementation and adoption. Nice to have: Previous experience in product management, consulting, or solutions engineering, particularly in roles bridging business and technology; experience training non-technical teams in AI; and familiarity with Flutter and/or Ruby. Language skills: Spanish is considered an advantage, particularly for potential international collaboration. Fluency in additional languages is not required. Benefits: Fully remote work model, offering flexibility to work from Brazil. Opportunities to collaborate with international teams and contribute to AI initiatives across different organizations. Exposure to agentic AI platforms, advanced LLM technologies, and real-world enterprise AI implementations. Opportunities to influence product strategy and help shape scalable AI adoption practices. A collaborative technology environment that encourages knowledge sharing, continuous learning, and professional development. Opportunities to contribute to innovative projects with meaningful business impact. Note: Specific salary details, healthcare coverage, and additional financial benefits were not provided in the original job description.