Principal AI Engineer

Robots and Pencils

About The Position

We’re looking for a Principal AI Engineer to define and drive the technical direction of our AI/ML systems. This role is ideal for a deeply experienced engineer who leads from the front — shaping architecture at the system level, solving the hardest problems, and raising the bar for how AI gets built and operated across the organization. In this role, you will work on a cross-functional team, setting AI architecture standards, leading complex system design and implementation challenges, and solving integration problems that require both deep expertise and broad systems thinking. You’ll partner closely with leadership to define the AI roadmap and make high-stakes technical decisions that shape how our AI work scales across clients and over time. 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.

Requirements

7+ years of experience in AI/ML engineering
Strong proficiency in Python
Strong software engineering background, including system design, API design, code quality, and strong unit testing practices
Experience designing and working with distributed systems and event-driven architectures
Expertise in MLOps and AI infrastructure, including model versioning, monitoring, deployment automation, and reproducibility
Experience with Amazon Quick, ServiceNow integration, Jira integration, AWS Bedrock, Agentic AI/ML (prompt engineering, agent development), Natural language querying / analytics
Strong stakeholder communication skills, with the ability to translate technical depth across audiences
In-depth experience with AWS, especially AWS GenAI offering; working knowledge of other cloud platforms
Familiarity with both SQL and NoSQL databases, including scalable design patterns
Experience with workflow orchestration tools and asynchronous system operations
Hands-on experience with LLM systems, including prompt engineering, function/tool calling, multi-agent orchestration, RAG architectures, vector databases, embeddings, and streaming LLM responses
Experience with containerization (e.g., Docker) and cloud-native AI architecture patterns
Exposure to AI governance and compliance considerations in production environments

Responsibilities

Define AI architecture and technical strategy, and lead implementation across the full lifecycle from design through production
Build scalable ML platforms, pipelines, and workflow orchestration that support model development and event-driven, asynchronous operations at scale (e.g., SQS, EventBridge)
Architect and build LLM-powered systems, including prompt engineering, function/tool calling, multi-agent orchestration, RAG patterns, vector databases, embeddings, and streaming responses
Design and develop APIs and backend services that integrate AI capabilities with enterprise systems and third-party platforms
Lead model development, optimization, and the path from research to production, ensuring promising approaches translate into reliable, production-ready systems
Ensure AI reliability, security, and scalability across deployed systems, including logging, monitoring, and debugging in production environments
Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace
Co-define the AI roadmap with leadership, operating as a peer in strategic technical conversations
Communicate complex technical concepts clearly to engineering and non-engineering stakeholders alike, translating depth into decisions others can act on
Engage with product, engineering, and data teams to align AI work with broader business priorities
Define and champion AI engineering standards that shape how the organization builds and operates AI systems
Mentor across the organization, shaping engineering culture and developing the next generation of technical leaders
Own high-stakes architectural decisions that carry significant organizational and cross-engagement weight
Drive technical vision — defining not just what gets built, but how AI engineering evolves at R&P over time