Related experienceProgramming language and framework experience
ApplicationCross-layer code review
Understand how a change affects frontend, backend and infrastructure. Involve the right expertise when a part needs closer review.
Role skills
Role skills explorer
Choose a role to explore its skills, the experience they build on and areas to practice. The groupings describe the guide’s content.
HELM 1.0.1 · Updated
Role guideSoftware EngineerFrontend Engineer · Backend Engineer · Full-Stack EngineerHELM responsibilitiesProduct Engineer
Build, review and maintain software with AI agents
3Build on experience
3Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
50% of listed skills50% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceProblem-solving and algorithm skills
ApplicationArchitectural judgment
Recognize when a change conflicts with the architecture or creates security, reliability or maintenance problems.
Related experienceREST API and database experience
ApplicationSystem thinking
Trace how a change affects dependencies, interfaces and behavior in use.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEContext engineering
Give agents relevant context, clear constraints and examples of the result you need.
PRACTICEQuality evaluation at volume
Keep review thorough as output increases. Prioritize meaningful checks and keep the workload manageable.
PRACTICEProduct awareness
Check whether the change solves the user’s problem as well as meeting technical requirements.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Whiteboard algorithm challenges disconnected from how the team actually ships
Arbitrary years-of-experience gates tied to specific framework versions
Raw speed of manual typing or line count as a proxy for seniority
Syntax recall without examples of applying or checking that knowledge
Specialist knowledge without evidence of working across relevant boundaries
Role guideStaff / Principal EngineerStaff Engineer · Principal Engineer · Solutions ArchitectHELM responsibilitiesAI Architect
Set technical direction and clear boundaries for agent-assisted work
4Build on experience
2Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
67% of listed skills33% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceDeep distributed systems expertise
ApplicationSystem design mastery
Design the interfaces, data flow and operating limits for systems that include agents.
Related experienceDesigning and shipping systems at scale
ApplicationFailure mode thinking
Plan for incorrect outputs, repeated actions, stale instructions and unclear ownership, including how to detect and recover from failures.
Related experienceTrack record of mentorship
ApplicationTechnical communication
Explain architecture decisions through clear task boundaries, diagrams and instructions.
Related experienceLeading complex technical initiatives
ApplicationCross-domain judgment
Assess output quality and design risk across domains (backend, data, security, UX) when agents span them.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEOrchestration pattern expertise
Choose a workflow or agent pattern that fits the task, and explain why simpler options are insufficient.
PRACTICECost-performance reasoning
Compare model and routing choices using task quality, response time and cost.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Code volume without evidence of the decisions and results behind it
Language or framework expertise without examples of wider system judgment
Expectation that you personally implement every subsystem you design
Individual achievements without evidence of collaboration or knowledge sharing
LeetCode-style algorithm screens disconnected from system and agentic design
Role guideEngineering ManagerEngineering Manager · Development Manager · Team LeadHELM responsibilitiesEngineering Manager
Support team learning and review the results of agent-assisted work
3Build on experience
3Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
50% of listed skills50% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceVelocity tracking and capacity planning
ApplicationOutcome-based measurement
Choose measures that help explain user outcomes, quality and effort, and check what a change in a measure means.
Related experienceManaging engineering teams (5+ years)
ApplicationOrganizational design
Review responsibilities and handoffs, and try a team arrangement when there is evidence it could help.
Related experienceStakeholder communication
ApplicationCoaching for judgment
Help engineers explain their reviews, recognize limits and practice difficult decisions.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEAdoption management
Understand how the team uses agents, identify obstacles and agree useful learning or workflow changes.
PRACTICEChange leadership
Listen to concerns, explain the proposed change and involve people in deciding how to try it.
PRACTICEAgentic workflow fluency
Use Plan-Execute-Verify-Ship-Learn to find where work gets stuck and agree an improvement with the team.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Meeting facilitation without examples of helping a team make decisions
Project-management tool expertise without evidence of leadership in practice
Velocity tracking and capacity planning anchored in story points
Team growth framed primarily as headcount scaling
Performance management driven mainly by code output or merge volume
Role guideSRE / DevOps EngineerSite Reliability Engineer · DevOps Engineer · Infrastructure EngineerHELM responsibilitiesAI Reliability Engineer
Monitor agent behavior, cost and recovery alongside service reliability
2Build on experience
4Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
33% of listed skills67% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceSLA/SLO definition (availability and latency)
ApplicationObservability design
Monitor the quality and behavior of agent workflows as well as service availability.
Related experienceIncident response and blameless postmortems
ApplicationIncident response for AI systems
Adapting detection, communication, and postmortem practice when the trigger is an agent workflow rather than a failed deploy.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEAgent failure mode expertise
Recognize incorrect outputs, repeated actions and scope errors that ordinary health checks may miss.
PRACTICECost engineering
Attribute costs to workflows, set useful budget alerts and compare savings with any effect on quality.
PRACTICEGuardrail implementation
Translating policy into automated enforcement, from secret scanning and PII detection to safety classification and dependency rules.
PRACTICEGovernance enforcement
Check access, ownership records and budgets during operation. Coordinate with the people who maintain the shared infrastructure.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Purely infrastructure-focused experience with no application-layer or data-flow awareness
Expertise limited to container orchestration and CI/CD pipelines without ownership of behavioral or economic SLOs
Incident response habits that assume deterministic failure modes and static blast-radius models
Cost management confined to compute, storage, and network with no fluency in token economics and agent run patterns
Monitoring strategies that stop at binary up/down checks and miss drift, abuse, and quality erosion
Role guideQA Engineer / SDETQA Engineer · SDET · QA Lead · Test EngineerHELM responsibilitiesQA Engineer + Evaluation Lead
Check agent output and the product people use
3Build on experience
3Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
50% of listed skills50% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceTest plans and test cases
ApplicationProduct judgment
Assessing experience quality beyond functional pass/fail.
Related experienceAutomation frameworks (Selenium, Cypress, Jest)
ApplicationAutomation at scale
Build useful automated checks and plan the human review effort they leave.
Related experienceBug tracking and regression discipline
ApplicationCross-functional communication
Turning quality signals into concrete, prioritized feedback for engineering and product.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEEvaluation design
Choose meaningful examples and criteria for judging outputs that may have more than one acceptable answer.
PRACTICEDrift detection
Notice changes in quality over time, including problems individual test runs can miss.
PRACTICEStatistical thinking
Understand the limits of a sample and explain what the available evidence supports.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Test execution counts without examples of choosing and investigating meaningful checks
Depth in a single framework without judgment about what to automate and why
Quality defined only as absence of defects, ignoring intent and experience
Assumptions that all code is human-written, reviewed at human cadence, and stable between releases
Release checks without involvement in planning and reviewing the work
Role guidePlatform / Infrastructure EngineerPlatform Engineer · Infrastructure Engineer · DevOps EngineerHELM responsibilitiesPlatform Engineer
Provide dependable shared tools for agent-assisted work
3Build on experience
3Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
50% of listed skills50% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceMonitoring and alerting for clusters and services
ApplicationCost optimization
Token-level cost tracking, routing for economic efficiency, and caching strategies tuned to generative workloads.
Related experienceDeveloper tooling and inner-loop optimization
ApplicationPlatform thinking
Understand what teams need from shared tools, make those tools usable and respond to feedback.
Related experienceKubernetes, cloud, and IaC at scale
ApplicationScale engineering
Plan shared capacity, ownership and operating support as more teams use agent workflows.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEAI infrastructure
Model hosting, inference optimization, GPU management, and the workload patterns that distinguish LLM serving from stateless web tiers.
PRACTICEGovernance engineering
Registry, access control, and audit systems for autonomous operations, not only for human users and service accounts.
PRACTICESecurity at the agent layer
Secure connectivity between agents and production systems, secret lifecycle, and explicit boundaries when machines act with elevated scope.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Hosting experience without discussion of the workloads this role will support
Build success without evidence about behavior, reliability or cost in use
Tool expertise without examples of supporting the teams who use it
Cost savings without checking the effect on quality and reliability
Design assumptions that do not fit long-running or stateful agent tasks
Role guideProduct ManagerProduct Manager · Product Owner · Business AnalystHELM responsibilitiesProduct Manager
Make goals clear and help your team build what users need
4Build on experience
2Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
67% of listed skills33% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceWriting detailed PRDs and user stories
ApplicationPrecision requirements writing
Write requirements with enough context and examples for someone to implement and review them.
Related experienceProduct strategy and roadmap ownership
ApplicationProduct judgment at speed
Review priorities and quality when several agent-assisted changes are ready at once.
Related experienceBacklog prioritization by business value
ApplicationOutcome orientation
Review user outcomes alongside delivery measures and explain what each measure can tell the team.
Related experienceManaging stakeholder expectations
ApplicationData-informed iteration
Use the Learn phase to improve requirement templates and criteria based on what actually shipped and how users responded.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEAgent capability awareness
Know what agents do well and where human judgment must intervene, and shape requirements accordingly.
PRACTICEConstraint-based thinking
Explain the requirements and limits a solution must respect, and specify steps where the task needs them.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Document volume without evidence that the requirements helped the team
Planning-tool expertise without examples of product decisions
Status reporting without evidence of resolving questions or disagreements
Backlog activity without an explanation of priorities and outcomes
"Experience with Agile/Scrum" as a differentiating qualification on its own
Role guideProduct DesignerUX Designer · UI Designer · Product Designer · Interaction DesignerHELM responsibilitiesProduct Designer
Design clear experiences and review agent-generated interfaces
4Build on experience
2Additional skills
5Discuss in context
Skills linked to prior experienceOther skills in this guide
67% of listed skills33% of listed skills
Build on experience
Experience you can build on
These entries connect familiar experience with tasks in AI-assisted work. Use the examples to discuss what you know and what needs practice.
Related experienceDesign system familiarity
ApplicationDesign system architecture
Maintain reusable components, design values, states and examples at the detail implementation needs.
Related experienceUX/UI experience (3-5 years)
ApplicationUX judgment at volume
Review generated interfaces for task flow, timing, spacing, visual hierarchy and tone.
Related experienceImplicit accessibility awareness
ApplicationAccessibility engineering
Design for accessibility and combine automated checks with keyboard, assistive-technology and user testing where needed.
Related experienceResearch and usability testing
ApplicationCross-functional collaboration
Agree requirements and constraints with engineering and product, using examples to resolve ambiguity.
Additional skills
Other skills to discuss
These skills may also help with the work. The guide does not assign them to a previous skill or assume they are new to you.
PRACTICEDesign governance
Set clear review standards, check generated work and use findings to improve the design system.
PRACTICEMachine-readable specification
Expressing design intent in structured forms (token JSON, component APIs, interaction specs) agents can execute against.
Discuss in context
Signals that need more context
These signals alone do not show how someone approaches the work. Discuss relevant examples, decisions and learning alongside them.
Pixel-perfect production speed as the main proxy for ability
Portfolios judged primarily on screen count
Design-tool fluency without examples of the decisions behind the work
Expectation that one designer personally produces every screen in a feature
Visual trends without evidence of usability, consistency or user needs