Jobs at Robots and Pencils

• Set the standard for data architecture across warehouses, data lakes, and lakehouse environments, including medallion architecture and open table format strategy
• Design for data and AI governance, including data governance, lineage, quality, cataloging, access control for AI agents, and evaluation data

• Define and drive the product vision, strategy, and roadmap for GenAI and agentic AI solutions, connecting AI capabilities to enterprise business outcomes with explicit invest vs. defer tradeoffs
• Research how enterprise users interact with AI agents and where they lose trust — frame the riskiest assumptions as testable hypotheses and de-risk them first

• Provide delivery oversight across all agentic AI workstreams, making sure each is on track to the outcomes committed to the client
• Validate that each team is building the right solution architecturally, reviewing designs, diagrams, and tradeoffs closely enough to catch flaws early

• Design and build agentic workflows — reasoning loops, tool/function calling, and orchestration across single- and multi-agent architectures — with clear judgment on which pattern fits the problem and which doesn't
• Build and maintain RAG pipelines: chunking strategy, embeddings, vector search (OpenSearch), re...

• Provide technical oversight of all agentic AI workstreams, from problem framing and design through evaluation, deployment, and ongoing operation
• Define the technical strategy and reference architecture for agentic systems, including multi-agent orchestration, tool and function calling, RAG patterns, vector databases, embeddings, and streaming responses

• Define technical strategy and lead architectural design across cloud, data, and AI/ML systems for end-to-end engagements
• Architect scalable cloud-native solutions, including multi-cloud and hybrid strategies (e.g., AWS, Azure, GCP, Kubernetes)

• Design, implement, and deploy data and AI/ML systems on AWS end-to-end, from concept through production, including data lake pipelines, observability instrumentation, and optimization for performance, reliability, and cost
• Maintain and evolve platforms in production, monitoring for drift, debugging issues, and drivin...

• Define product strategy and vision for major client engagements, setting direction in alignment with client goals and business outcomes
• Own the product roadmap end-to-end, driving prioritization across business value, technical constraints, and delivery timelines

• Design and implement scalable data pipelines, taking ownership of features end-to-end
• Build and manage data warehouses and data lakes

• Design, implement, and deploy ML/AI models end-to-end, from concept through production, including data pipelines, training workflows, and optimization for performance, accuracy, and efficiency
• Maintain and evolve AI systems in production, monitoring for drift, debugging issues, and driving ongoing improvements to reliability and scalability

• Own end-to-end product delivery for complex AI and agentic AI engagements, from discovery and hypothesis framing through phased rollout and production readiness
• Define and maintain AI product roadmaps that connect agent capabilities to measurable enterprise business outcomes

• Define technical strategy and lead architectural design across cloud, data, and AI/ML systems for end-to-end engagements
• Architect scalable cloud-native solutions, including multi-cloud and hybrid strategies (e.g., AWS, Azure, GCP, Kubernetes)

• Define security architecture and engineering standards across web, API, and cloud-native systems, leading the hardening of complex services
• Lead threat modeling and security design reviews across applications and AI systems, setting how risk is identified and prioritized

• Contribute to architectural design across cloud, data, and AI/ML systems, taking ownership of meaningful components
• Build and integrate cloud-native solutions including microservices, serverless, and containerized workloads (e.g., AWS, Azure, GCP, Kubernetes, Docker)

• Train and operate models on Amazon SageMaker with AWS Trainium as the underlying compute
• Write and optimize PyTorch training code with a real understanding of how it compiles and executes on Trainium (NeuronCore architecture, compiler behavior, memory and throughput tradeoffs)

• Define DevOps strategy and lead infrastructure architecture across multi-environment, multi-region cloud systems
• Architect and own scalable Kubernetes platforms and containerized infrastructure at scale

• Define product strategy and vision for major client engagements, setting direction in alignment with client goals and business outcomes
• Own the product roadmap end-to-end, driving prioritization across business value, technical constraints, and delivery timelines

• Design end-to-end UX/UI deliverables from early exploration and concepting through high-fidelity execution
• Create wireframes, prototypes, and polished interface designs that balance usability, business goals, and technical constraints

• 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

We're looking for a Principal AI Engineering Architect to lead the design and delivery of complex, multi-domain systems spanning cloud, data, and AI — with deep, hands-on mastery of multi-agent agentic AI solutions. This role is ideal for a deeply experienced engineer who owns the hardest archite...