Principal AI Engineer

Gugu Robotics

Principal AI Engineer at Robots and Pencils driving the technical direction of AI/ML systems. Leading complex system design and implementation with a cross-functional team.

Posted 6/9/2026full-timeRemote • 🇺🇸 United StatesLeadWebsite

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsDockerNoSQLPythonServiceNowSQL

About the role

Key responsibilities & impact

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
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

Requirements

What you’ll need

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

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible working hours
Paid time off
Remote work options

ATS Keywords

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Hard Skills & Tools

PythonMLOpsAPI designdistributed systemsevent-driven architecturemodel versioningdeployment automationSQLNoSQLcontainerization

Soft Skills

stakeholder communicationmentoringtechnical visionstrategic technical conversationsengineering culture development