Staff Forward Deployed AI Engineer

We're looking for a Staff Forward Deployed AI Engineer to lead the design, delivery, and adoption of AI/ML systems directly within client environments. This role is ideal for an experienced engineer who thrives on architectural decisions, can own systems end-to-end, and drives real outcomes at the intersection of technical depth and client partnership.

In this role, you will embed with clients as a key technical leader, defining AI architecture, leading model development and optimization, and solving challenging integration problems in live enterprise environments. You'll parachute into in-flight work where reliability, security, and scalability are critical — and you'll be the person who makes it land. You'll raise the bar on how we build and deploy AI systems and help clients actually adopt and scale what we ship.

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 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

Collaboration & Client Partnership

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

Leadership & Influence

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

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
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