Forward Deployed Engineer AI

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Forward Deployed Engineer AI
- Sênior based in Brazil. In this senior role, you will bridge the gap between business strategy, product thinking, and AI engineering to help organizations unlock the value of agentic AI. You will work closely with business teams to understand their workflows, identify meaningful opportunities, and design AI agents that solve real-world challenges. Using your technical expertise, you will guide the implementation of end-to-end solutions on an existing agentic AI platform, including integrations with business systems and data sources. Beyond building initial use cases, you will empower non-technical teams to create, manage, and continuously improve their own AI agents. You will also monitor adoption, performance, business impact, and costs to ensure sustainable results. This is an opportunity to shape AI adoption at scale, influence product evolution, and establish best practices that can be replicated across multiple organizations. 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 so