Gugu Robotics

Backend Engineer

• Help build and maintain modern applications that integrate backend services, orchestration systems, and scalable APIs.
• Work across the stack using Node.js, contributing to backend services and supporting frontend integrations.
• Collaborate closely with senior engineers to enhance existing systems and deliver high-quality features.
• Support the development and maintenance of workflows using orchestration tools.
• Develop RESTful APIs and support microservices architecture.
• Troubleshoot issues in production environments and contribute to logging, monitoring, and debugging efforts.
• Take ownership of assigned features and tasks, delivering end-to-end solutions from UI to API to database.

Senior Fullstack Engineer, Node.js

AI Engineer

• 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

Principal AI Engineer

Architect

• 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 data architectures including warehouses, data lakes, and pipelines for batch and streaming workloads (e.g., Snowflake, Redshift, BigQuery, Spark, Kafka)
• Design AI/ML systems including model serving, MLOps pipelines, feature stores, and LLM-based applications (e.g., SageMaker, Vertex AI, MLflow, Hugging Face)
• Define infrastructure as code, CI/CD, and DevOps standards across engagements (e.g., Terraform, CloudFormation, GitHub Actions)
• Drive performance, scalability, cost, and reliability optimization across deployed systems
• Ensure architecture meets security, governance, and compliance requirements (e.g., GDPR, HIPAA, SOC2)
• Lead cloud migrations and platform modernization initiatives
• 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
• Partner with senior leadership and clients as the principal technical voice on strategy and direction
• Communicate complex technical concepts clearly to engineering and non-engineering stakeholders alike
• Engage closely with engineering, data, AI, and product teams to align architecture with broader business priorities
• Develop and maintain architecture documentation, standards, and guidelines
• Define and champion architectural standards and best practices on the engagements you support
• Mentor experienced engineers and architects, helping them grow their technical judgment and impact
• Own the most difficult architectural and integration challenges, serving as the senior technical decision-maker
• Evaluate emerging technologies and recommend tools, frameworks, and patterns that improve architecture over time.

Principal Architect

Data Engineer

• Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes
• Build and optimize scalable data pipelines supporting batch and real-time processing
• Define and enforce data governance, quality standards, and compliance frameworks across the platform
• Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation
• Drive data platform modernization, optimizing for performance, cost, and scalability
• 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
• Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams
• Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
• Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption
• Partner with leadership on data strategy, translating technical depth into decisions others can act on
• Collaborate closely with engineering, analytics, AI, and product teams to align data platforms with broader goals
• Advocate for data quality, governance, and platform best practices across teams and engagements
• Establish data engineering standards that lift the quality and consistency of work across the team
• Mentor junior and mid-level engineers, helping them grow their craft, confidence, and impact
• Make high-stakes architectural decisions with clear ownership and consideration of long-term tradeoffs

Staff Data Engineer

Data Scientist

• Design and implement scalable and reliable approaches to support or automate decision making throughout the business
• Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear
• Acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems
• Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies
• Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks
• Validate models against alternative approaches, expected and observed outcomes, and other business defined key performance indicators
• Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production
• Implement and deploy state of the art machine learning algorithms under Gen AI, build prototypes, troubleshoot customer issues, and explore new solutions
• Interact closely with customers and with the academic community to drive innovation and deliver tailored data science solutions

• Build and maintain backend services using Node.js (and some Python), including serverless architectures (e.g., AWS Lambda, API Gateway)
• Develop and integrate APIs that support frontend applications and external systems
• Contribute to frontend features and ensure smooth integration across the stack
• Write clean, maintainable, and well-tested code with solid unit testing practices
• Contribute to workflows using orchestration tools (e.g., workflow engines, state machines)
• Support event-driven processes and asynchronous system operations (e.g., SQS, EventBridge)
• Help improve system reliability through error handling and retries
• Develop RESTful APIs and contribute to backend architecture
• Integrate with third-party services and internal systems, including AI-driven platforms
• Assist in optimizing performance and scalability of services (e.g., DynamoDB patterns)
• Follow secure and reliable data handling practices
• Troubleshoot issues in production environments with support from senior team members
• Contribute to logging, monitoring, and debugging efforts
• Assist with deployment and release processes in cloud-native environments (AWS, Docker)
• Work closely with cross-functional teams across global locations
• Participate in code reviews and team discussions
• Deliver assigned features end-to-end with guidance
• Continue developing technical skills, including AI-assisted development tools

Fullstack Engineer, Node.js

• Build and maintain backend services using Node.js
• Develop and integrate APIs that support frontend applications
• Contribute to frontend features using modern state management
• Write clean, maintainable, and well-tested code
• Support the development and maintenance of workflows
• Contribute to event-driven processes and asynchronous system operations
• Assist in improving reliability through better error handling
• Develop RESTful APIs and support microservices architecture
• Integrate with third-party services and internal systems
• Assist in optimizing performance and scalability of services
• Implement secure and reliable data handling practices
• Troubleshoot issues in production environments
• Contribute to logging, monitoring, and debugging efforts
• Support deployment and release processes in cloud-native environments
• Work closely with cross-functional teams

ServiceNow

• Define front-end architecture and technical strategy
• Lead back-end development and API architecture
• Design and implement integrations with ServiceNow and SharePoint
• Build and maintain data pipelines on AWS
• Architect and operate AWS infrastructure supporting pipeline and application workloads
• Lead monitoring, observability, and reliability practices across production systems
• Mentor junior and mid-level engineers

Staff Full Stack Engineer, ServiceNow

Full-stack Engineer

• Define front-end architecture and technical strategy, designing scalable platforms and leading complex UI systems development (e.g., React, Angular, Next.js)
• Build polished, demo-ready front-end experiences that communicate the value of AI systems to clients and stakeholders
• Define front-end standards and best practices, and contribute to shared design systems
• Lead back-end development and API architecture in Python and Node.js, integrating complex systems and third-party platforms
• Design and implement channel integrations across chat, voice, and web surfaces, including real-time messaging, webhook pipelines, and event-driven patterns
• Design efficient database schemas and optimize performance across relational and NoSQL systems
• Drive backend performance, scalability, and reliability, and own implementation of security best practices including authentication and authorization
• Lead monitoring, observability, and reliability practices across production systems
• Lead the design and integration of AI services into production full-stack systems, including LLM APIs, streaming, tool and function calling, multi-step agent flows, and RAG patterns
• 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

Staff Full Stack Engineer

Client Partner

• Serve as executive sponsor for strategic accounts, building and maintaining C-level client relationships.
• Act as a trusted advisor, helping clients align business strategy with digital innovation and transformation opportunities.
• Lead client strategy reviews, roadmaps, and executive QBRs, ensuring alignment of Robots & Pencils’ work with measurable business outcomes.
• Oversee end-to-end delivery of complex, multi-phase engagements, ensuring alignment across scope, budget, timeline, and quality.
• Identify and proactively manage dependencies, risks, scope changes, and escalations before they become blockers.
• Manage phased releases, production readiness, and transitions into operations and maintenance models.
• Bring an AI-forward mindset to your daily work, using tools like Claude to enhance delivery planning, reporting, and stakeholder communications.
• Own overall revenue growth, retention, and profitability targets across the client portfolio.
• Partner with sales and practice leads to identify new opportunities, shape proposals, and drive expansion in existing accounts.
• Negotiate executive-level agreements, renewals, and long-term partnerships.
• Develop and implement strategies to strengthen account penetration and expand Robots & Pencils’ industry footprint.
• Lead and develop the account management organization, including directors and senior managers.
• Establish scalable account management processes, frameworks, and playbooks across the company.
• Partner with delivery leadership to ensure consistent alignment between account strategy and delivery execution.
• Represent the client voice at the executive table, influencing Robots & Pencils’ overall business strategy.
• Build multi-year account plans for top clients, aligning with their evolving needs and Robots & Pencils’ growth goals.
• Monitor client portfolio health through KPIs such as revenue growth, retention, NPS, and profitability.
• Collaborate with finance, marketing, and operations to forecast revenue, plan resourcing, and shape go-to-market initiatives.
• Identify market and client trends, shaping Robots & Pencils’ positioning and offerings.

Product Manager

• Translate institutional priorities into executable sprint work
• Own stakeholder engagement
• Drive delivery decisions
• Build the client's internal product ownership capability over the course of the engagement
• Work embedded within cross-functional delivery teams

Manager

• Keep the pod focused, moving, and accountable.
• Own the delivery operating model — sprint cadence, dependency management, risk identification, and stakeholder communication.
• Work across both the R&P team and client-side counterparts, maintaining momentum through ambiguity.
• Surface blockers before they become problems, ensuring the pod delivers against priorities week over week.

Delivery Manager

• Lead delivery of an AI-powered student intervention system being built for a large public research university.
• Manage a cross-functional team, coordinate across multiple institutional stakeholders and internal systems, and keep delivery moving in an environment where scope and integration dependencies are being defined in real time.
• Responsible for translating discovery findings into a governed build plan, maintaining alignment between technical progress and institutional access timelines.
• Ensure the system that reaches students is both operationally sound and set up for continuous improvement.
• Comfortable with ambiguity, experienced managing delivery in institutional environments.

Technical Product Manager

• Manage the product backlog, ensuring work is clearly defined, prioritized, and aligned to business outcomes
• Translate business needs and stakeholder requests into structured requirements, user stories, and acceptance criteria
• Partner closely with engineering and design to ensure requirements are well understood and delivered with quality
• Support sprint planning, backlog refinement, and day-to-day execution to keep delivery on track
• Identify and manage dependencies, risks, and scope changes throughout the delivery lifecycle
• Elicit, analyze, and document functional and technical requirements across multiple stakeholders
• Break down complex business processes into clear, actionable work for delivery teams
• Ensure traceability between business needs, requirements, and delivered solutions
• Continuously refine requirements based on feedback, testing, and evolving priorities
• Support QA and UAT efforts by clarifying expected behavior and validating outcomes
• Facilitate working sessions, requirement discussions, and decision-making forums
• Communicate progress, risks, and tradeoffs clearly to both technical and non-technical audiences
• Build strong relationships with client stakeholders and serve as a trusted partner in delivery
• Frame requests in terms of user and business outcomes, not just features
• Support prioritization decisions by balancing business value, technical constraints, and delivery timelines
• Contribute to defining scope and iterative delivery plans
• Identify opportunities to improve processes, workflows, and product experience
• Leverage data, feedback, and team insights to continuously improve the product

• Define and drive the product vision, strategy, and roadmap for GenAI solutions - with agentic AI (agent orchestration, tool use, multi-step workflows) as the primary focus - connecting AI capabilities to enterprise business outcomes
• Translate enterprise problems into structured product requirements; reframe feature requests into outcome-driven priorities with explicit tradeoffs on invest in vs. defer
• Balance near-term deployment milestones with long-term platform scalability and sustainability
• Monitor the competitive GenAI landscape and emerging agentic patterns to inform roadmap and technology decisions
• 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
• Design and run experiments - POCs, pilot deployments, scenario-based testing of multi-step workflows, edge cases, and failure recovery - to validate agentic solutions where non-deterministic output makes traditional QA insufficient
• Distill research, experiments, and competitive intelligence into clear insights that pave the path for a successful product
• Define agent behavior and prototype system prompts and tool schemas; partner with engineering on context management - summarization, working memory, and information flow across multi-step tasks
• Drive multi-model architecture tradeoffs with engineering - define the quality, cost, and latency targets that determine which model serves each step in the agent workflow
• Build AI prototypes to validate hypotheses; define human-in-the-loop boundaries and guardrails - when the agent acts autonomously, when it escalates, and how to handle non-deterministic output
• Establish agent evaluation frameworks - task completion, reasoning quality, tool selection, failure recovery, safety - and partner with engineering on production readiness (observability, drift, responsible AI, prompt versioning)
• Define success metrics at the agent level - task completion rate, cost per task (not per inference), escalation rate, time to resolution, and customer trust alongside business KPIs
• Own the end-to-end product lifecycle from discovery through phased rollouts; establish the metrics framework (north star, input, guardrail metrics) and report product impact to leadership
• Manage the product backlog, scope, dependencies, and risks; drive agile ceremonies and produce high-quality PRDs, product briefs, and decision logs
• Evaluate technology and platform decisions from a product perspective; create deployment playbooks, reference architectures, and knowledge transfer materials so teams sustain solutions independently
• Use AI to accelerate product work - research, analysis, prototyping, documentation - with judgment on when it needs human oversight; onboard rapidly to new domains and support team members across the initiative
• Build trusted relationships with stakeholders and executives; serve as the go-to product advisor and primary contact for AI product direction and deployment strategy
• Partner with AWS Solution Architects and account teams to align on technical approach, service selection, and go-to-market for GenAI solutions
• Manage expectations on scope, timelines, and tradeoffs; facilitate decisions across competing priorities using data, alternatives, and clear rationale
• Frame AI capabilities and limitations for non-technical stakeholders - manage hype cycles, set realistic expectations; surface unmet needs that deepen relationships and grow the account.