[Hiring] Principal AI Engineer @Robots and Pencils

Role Description

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.

What You’ll Do

• Craft & Delivery
  • 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.
• Collaboration & Communication
  • 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.
• Leadership & Influence
  • 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.

Qualifications

• 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.