Forward Deployed Engineer

🕒 Julho 29

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $177.375 - $209.625 / ano

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

👷🏻‍♀️ Engenheiro

👻 Score fantasma 4%

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Robots & Pencils

SiteLinkedInTodas as Vagas

51 - 200 funcionários

🤖 Inteligência Artificial

🤝 B2B

☁️ SaaS

💰 Venture Round em 2022-04

Artificial Intelligence • B2B • SaaS

A Robots & Pencils é uma empresa de inovação digital especializada no desenvolvimento de estratégias digitais e produtos utilizando tecnologias móveis, web e de ponta para impulsionar a transformação das empresas. Eles se concentram em áreas como desenvolvimento móvel, design de produto, pesquisa de UX, inteligência artificial, machine learning e gestão de mudanças organizacionais. Com o compromisso de combinar criatividade com tecnologia, eles visam desbloquear insights de dados e aprimorar a inovação em produtos, entregando, assim, excelentes experiências aos clientes e ajudando as marcas a obter uma vantagem competitiva.

Descrição

• Lead the design and implementation of complex ML/AI systems end-to-end within client environments, owning architecture decisions and driving solutions from research through production at scale • Build, deploy, and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development and deployment — often within existing client constraints • Diagnose and unblock integration challenges in live systems, adapting quickly to unfamiliar codebases, cloud environments, and organizational contexts • Set the standard for AI-forward engineering, using tools like Claude and Cursor with sophistication and helping both our team and client teams adopt them effectively • Embed directly with client teams, earning trust quickly and translating their business problems into AI system designs that actually work in their environment • Partner with product, engineering, and leadership — ours and the client's — to align technical direction with business outcomes • Translate complex AI tradeoffs, risks, and opportunities into clear narratives for technical and non-technical stakeholders • Lead design reviews and technical discussions, raising the bar for engineering rigor across both R&P and client teams • Define AI architecture and engineering standards for each engagement, bringing depth on tradeoffs, long-term implications, and responsible AI practices • Drive client adoption of AI systems post-delivery — identifying gaps in understanding, building enablement materials, and upskilling client engineers where needed • Mentor and grow junior and mid-level engineers, multiplying impact through coaching, code reviews, and pairing on hard problems • Take ownership of the most ambiguous and highest-stakes pieces of work, driving them through to production with care for reliability, cost, and safety

🎯 Requisitos

• 7+ years professional software engineering experience, with 4+ years focused on AI/ML systems in production and deep hands-on experience with generative AI development • Expert software engineering background (Python or similar) with strong design sensibilities for scalable, maintainable systems • Deep expertise with cloud platforms, including in-depth understanding of AWS services and AWS GenAI offerings • Proven track record designing and shipping complex agentic systems in production environments, including within client or enterprise constraints • Mastery of AI frameworks and orchestration tools • Strong experience with evaluation frameworks and observability tools for LLM apps, including building these capabilities where they don't yet exist • Deep understanding of AI safety, responsible AI principles, prompt injection defenses, and PII handling • Extensive experience building RAG pipelines: chunking strategies, embedding models, vector databases, and advanced retrieval techniques • API design experience, including architecting and integrating with internal and third-party services at scale • Advanced cost optimization expertise: token economics, caching strategies, model routing, quantization • Strong working knowledge of Docker and Kubernetes for containerized deployments • Demonstrable, day-to-day usage and expert knowledge of AI-forward coding tools such as Claude Code and Cursor • Comfort operating in ambiguous, fast-moving client environments — adapting quickly, communicating clearly, and earning trust at the engineering level.