Staff AI Engineer

We’re looking for a Staff AI Engineer to lead the design and delivery of AI/ML systems. This role is ideal for an experienced engineer who thrives on architectural decisions, can confidently own systems end-to-end, and contributes to technical leadership across the team. In this role, you will work as a key technical leader on a cross-functional team, defining AI architecture, leading model development and optimization, and solving challenging integration problems. You’ll be joining real, in-flight work where reliability, security, and scalability are critical. You’ll establish engineering standards, raise the bar on how we build AI systems, and contribute to the technical decisions that shape how our AI work scales over time. Why This Role Matters At Robots & Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every agent we ship has to work for real people in real workflows. That balance is baked into how we operate. Every role here contributes directly to that mission. Here, you shape how AI systems integrate into enterprise operations, how teams move at real velocity, and how products create measurable impact for clients and the people they serve. We ship production-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on. What You’ll Do Craft & Delivery Lead the design and implementation of complex ML/AI systems end-to-end, owning architecture decisions and driving solutions from research through production at scale Build and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development and deployment across teams Set the standard for AI-forward engineering, using tools like Claude and Cursor with sophistication and helping the team adopt them effectively Collaboration & Communication Partner with product, engineering, and leadership to shape AI strategy and align technical direction with business outcomes Translate complex AI tradeoffs, risks, and opportunities into clear narratives that drive decision-making across technical and non-technical stakeholders Lead design reviews and technical discussions, raising the bar for engineering rigor and constructive challenge across the team Leadership & Influence Define AI architecture and engineering standards, bringing depth on tradeoffs, long-term implications, and responsible AI practices 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 What You’ll Bring 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. Experience with Amazon Quick, ServiceNow integration, Jira integration, AWS Bedrock, Agentic AI/ML (prompt engineering, agent development), Natural language querying / analytics. 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 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 Experience mentoring engineers and shaping AI engineering practices across a team or organization Excellent problem-solving skills and the ability to navigate highly ambiguous technical and business challenges with sound judgment

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