AI-First Expertise Scoring
Scored across ten expertise dimensions - from Strategic Clarity & Long-Term Vision to Innovation & Growth Mindset - built from your CV, LinkedIn and voice interviews.
Impact-Site-Verification: 7aabcb1a-0a22-4299-b26c-e6cbb9b275a6
Ten AI-Native Engineering Areas,
Ten Expertise Dimensions, One Reviewed Profile
AI-First Match and our AI Experts run the technical interview and score how people actually build with AI - agentic engineering practice, system design, and product thinking - and our CTO & Principal Engineer Network reviews every result before a company sees it.
Every CV now claims AI fluency. Almost nothing in a hiring process can tell you whether it is true.
A CV can claim "AI-first." It cannot show whether you orchestrate agents, gate AI code review, sandbox what an agent is allowed to touch - or whether the system you design around all of it holds up in production. AI-First Match and our AI Experts score exactly that - and deliberately not from your CV. The scorecard is built only from what you actually said in a voice interview.
Then a human who has built this for a living checks the result. As the evidence gets stronger, so does what companies see.
No evidence, no score. Every rated area cites a verbatim quote from your own interview. Areas the conversation never reached come back marked Unverified - never guessed, never padded. Every profile and every assessment is then reviewed by our CTO & Principal Engineer Network before it reaches a company.
§ 01The Thesis
Capability is no longer the constraint. Frontier models can already plan, write, review and ship production code. What's missing is the human layer - product engineers, powered by agentic AI, with the system design judgment to know what to delegate to an agent, what stays deterministic, and what still needs a person in the loop.
Companies cannot wait. The next scarce asset is not model access - it is the product engineer who turns solid system design thinking into successful product platforms, and can show the receipts when asked. Agentic AI skill alone is not enough.
§ 02The Shift
AI exposure is highest across technology, management, finance and legal work. Highly AI-exposed occupations are projected to grow more slowly - but the people who redesign those functions will define the next decade.
The market is no longer paying for output at the old margins. It is paying for the smaller group who can reshape a workflow around AI - and prove the result rather than assert it.
AI-First Match is the assessment layer for this transition - AI scores AI-first expertise from real interview evidence, our CTO & Principal Engineer Network reviews every result, and we match experts with the companies hiring for exactly that.
Operating thesisScored across ten expertise dimensions - from Strategic Clarity & Long-Term Vision to Innovation & Growth Mindset - built from your CV, LinkedIn and voice interviews.
Ten areas that separate AI users from AI-native engineers: coding agents, specs and reusable skills, agent orchestration, AI code review, MCP integration, agent isolation, team adoption, system design, ownership and learning, and stakeholder collaboration.
A single 45-60 minute voice interview covers what used to take two calls - your agentic AI practice, system design decisions and product thinking, scored against a real target role. Sharpen in a mock run first.
Always-on chat and voice mentor for career strategy, expertise coaching and workflow-transformation advice - grounded in your own scores, not generic career tips.
Upload your CV or connect LinkedIn. AI parses and enriches your profile, identifies the gaps holding your score down, and generates targeted recommendations.
Your data is yours - full deletion at any time under GDPR Article 17. And because AI in hiring is high-stakes, the platform is validated against the EU AI Act: human oversight on every assessment, transparent evidence behind every score.
Shortlisted experts are ranked by a composite match score built from their scored interview evidence and hire recommendation - not from keyword overlap on a CV.
One 45-60 minute AI-led call is both the recruiter screen and the technical interview - probing agentic AI practice, system design and product thinking against fixed rubric anchors. Repeatable, comparable, and audio-only by design: no camera, no facial analysis.
Every profile and every AI assessment is reviewed by our CTO & Principal Engineer Network - agentic AI builders with strong software engineering backgrounds. You get a scorecard a practitioner has already signed off on, not raw model output.
Full area breakdowns, interview transcripts, the verbatim quote behind every score, and a level placement from L3 through L8+ - exportable as a PDF report for your hiring team.
Titles change faster than skills. Whether the req says Agentic AI Engineer, AI Product Engineer or AI Builder, AI-First Match runs the same 45-60 minute technical interview and scores the same evidence: ten AI-native engineering areas - system design included - and product thinking - reviewed by our CTO & Principal Engineer Network.
Designs and ships multi-agent systems. Scored on coding agents, agent orchestration & context, and agent isolation & human oversight.
Builds AI solutions inside customer environments, under real constraints. Scored on product thinking, pragmatic trade-offs and delivery ownership.
Builds the scaffolding agents run in - harnesses, tools, sandboxes, permissions. Scored on agent isolation, MCP & tool integration and quality gates.
Designs the evals that decide whether AI output ships. Scored on AI code review & quality gates and evidence-driven engineering judgment.
Engineers what the model sees - context pipelines, retrieval, specs and reusable skills. Scored on specifications & reusable skills and orchestration context.
Owns a product outcome end to end and builds it with agentic AI. Scored on product thinking and the full ten-area scorecard, system design included.
Builds the internal platform other teams build agents on. Scored on system design, MCP & external tool integration and isolation & oversight.
Keeps agent fleets observable, safe and reliable in production. Scored on human oversight, fallbacks and quality gates.
Redesigns business workflows around AI rather than bolting AI onto them. Scored on AI-native workflow redesign and adoption & team communication.
The generalist who takes an AI product from idea to shipped. Scored across all ten AI-native areas, system design included, plus product thinking.
The titles overlap; the jobs do not. Each of these ten roles exists to answer a different question in production - so compare what each one is for, what it builds, how it relates to agent orchestration, and what it answers for once the system ships. Read across for one role; read down a column to compare all ten.
| Role & mission | The question it answers | Core responsibilities | Agent orchestration | Production focus |
|---|---|---|---|---|
| 01 Agentic AI Engineer Builds agents that complete multi-step tasks. | “How can the agent complete this task with appropriate autonomy?” | Implements planning, tool use, business logic, memory and human handoffs. | Designs task-specific agent loops, delegation and coordination. | Task success, correct actions, bounded autonomy and recovery from failures. |
| 02 Forward Deployed AI Engineer Delivers working AI solutions inside customer environments. | “How do we solve this customer’s problem and get the solution adopted?” | Discovers requirements, integrates customer systems, customizes solutions and deploys them. | Adapts workflows to customer processes, permissions and existing infrastructure. | Customer outcomes, adoption, time to value and operational handover. |
| 03 AI Harness Engineer Builds the execution environment around models and agents. | “What enables the agent to work effectively, within controlled boundaries?” | Builds tool execution, session management, checkpoints, sandboxing and feedback mechanisms. Anthropic on harness design | Implements execution loops, stopping conditions, retries and resumable sessions. | Predictable execution, isolation, recoverability and effective long-running work. |
| 04 AI Evals Engineer Produces trustworthy evidence of AI quality and limitations. | “How do we know this change improves behavior without introducing regressions?” | Builds datasets, scoring rubrics, graders, repeatable experiments and regression gates. Anthropic on agent evals | Evaluates tool choices, handoffs, trajectories and final outcomes; rarely owns business orchestration itself. | Regressions detected, uncertainty quantified, release decisions informed by evidence. |
| 05 Context Engineer Gives models the right information at the right time. | “What should the agent know at this step?” | Designs retrieval, instructions, memory, context selection, compaction and information provenance. Anthropic on context engineering | Controls the information passed between steps and agents, including context isolation. | Relevant, fresh, permission-aware information within token and latency budgets. |
| 06 AI Product Engineer Owns useful AI-powered product experiences end to end. | “What should we build so users get meaningful value?” | Combines product discovery, UX, full-stack development, model integration and experimentation. | Builds or integrates agent workflows when they improve the product; agents are optional. | User outcomes, adoption, experience quality, latency and cost. |
| 07 AI Agent Platform Engineer Provides reusable infrastructure for many agents and teams. | “How can teams build and deploy agents consistently, at scale?” | Builds shared APIs and SDKs, deployment services, registries, access controls and tenant isolation. | Provides the shared scheduling, state storage and execution services application teams build on. | Platform availability, scalability, governance and developer productivity. |
| 08 AgentOps / AI Reliability Engineer Keeps deployed agents dependable and operationally manageable. | “Are agents working reliably in production, and what happens when they fail?” | Implements tracing, alerts, service objectives, incident response, rollback and cost monitoring. LangSmith observability docs | Monitors executions; diagnoses loops, stalled runs, retry storms and broken dependencies. | Availability, successful execution, recovery time, latency and operating cost. |
| 09 AI Business Automation Engineer Improves internal business processes with AI and automation. | “Which workflow can we improve, and how much measurable value will it create?” | Maps processes, integrates business systems, implements automation and designs approval and exception handling. | Connects agents, deterministic workflows and human approvals across departments. | Reduced processing time, fewer errors, auditability and employee adoption. |
| 10 AI Builder Turns ideas into working products with AI-assisted development. | “How quickly can I build and validate something useful?” | Prototypes, assembles services, directs coding agents, tests outputs and iterates with users. | Often directs coding agents that build the software; the resulting product need not contain agents. | Fast validation and usable delivery; production ownership varies with the actual role. |
However the req is titled, the interview is the same: one 45-60 minute call scoring ten AI-native areas - system design included - and product thinking - reviewed by our CTO & Principal Engineer Network before a company sees it.
Your profile is parsed, structured and enriched by AI, then scored across ten AI-first expertise dimensions - with the gaps named, not just the totals.
One 45-60 minute voice call that is both the screening and the technical interview - your agentic AI practice, system design decisions and product thinking. It adapts to what you claim, follows up where the evidence is thin, and mock runs let you practise first.
Ten areas scored 0-10 from the transcript alone, each backed by a verbatim quote, with a level placement from L3 to L8+. Whatever the conversation never reached comes back Unverified - never estimated to fill the page.
Every profile and every AI assessment is reviewed by our CTO & Principal Engineer Network - agentic AI builders with strong software engineering backgrounds. Nothing reaches a hiring team as raw model output.
Always-on coaching grounded in your own scores: what to sharpen, in what order, and what evidence would move the number next time.
Browse AI-first roles ranked by fit against your scored profile, and see where you stand - and what would move you up - before you apply.
AI's theoretical coverage of professional tasks already approaches saturation. What organisations actually use is a fraction of that. The delta is the opportunity surface for AI-first experts - and the reason verifying them properly matters.
Data · Anthropic · March 2026
AI-First Match is an AI-first expert assessment and matching platform. One 45-60 minute AI-led voice call serves as both the recruiter screening and the technical interview - evaluating agentic AI practice, system design and product-engineer thinking - scored across ten AI-first expertise dimensions and a ten-area AI-native engineering scorecard. Every profile and every assessment is reviewed by our CTO & Principal Engineer Network before it reaches a company.
AI-First Match serves two sides: AI-first experts - product engineers and senior specialists who build with agentic AI and want that verified and matched - and the CTOs, hiring managers and recruiters who need technical screening they can trust without spending their team's hours to get it. Recruiting agencies use the same assessment layer to build structured AI-first talent pipelines.
AI-First Match assesses, interviews and matches candidates for the new generation of AI-first engineering roles: Agentic AI Engineer, Forward Deployed AI Engineer, AI Harness Engineer, AI Evals Engineer, Context Engineer, AI Product Engineer, AI Agent Platform Engineer, AgentOps / AI Reliability Engineer, AI Business Automation Engineer and AI Builder. Titles vary faster than skills - so every role is scored against the same evidence standard: ten AI-native engineering areas - system design included - and product-engineer thinking, reviewed by our CTO & Principal Engineer Network.
Each role answers a different question in production. An Agentic AI Engineer builds agents that complete multi-step tasks; a Forward Deployed AI Engineer delivers AI solutions inside customer environments; an AI Harness Engineer builds the execution environment around models and agents; an AI Evals Engineer produces trustworthy evidence of AI quality; a Context Engineer gives models the right information at the right time; an AI Product Engineer owns AI-powered product experiences end to end; an AI Agent Platform Engineer provides reusable infrastructure for many agents and teams; an AgentOps / AI Reliability Engineer keeps deployed agents dependable in production; an AI Business Automation Engineer improves internal business processes with AI; and an AI Builder turns ideas into working products with AI-assisted development. The role comparison matrix on this page sets all ten side by side - mission, core responsibilities, agent orchestration and production focus.
Ten areas that separate people who use AI from people who build AI-native workflows: coding agents such as Claude Code and Codex; specifications and reusable skills; agent orchestration and context management; AI code review and quality gates; MCP and external tool integration; agent isolation and human oversight; AI adoption and team communication; system design and architectural judgment; ownership, self-awareness and learning; and collaboration and stakeholder communication. Each scored area must cite at least one verbatim quote from your interview transcript - areas the conversation never reached are returned as Unverified rather than estimated - and the finished scorecard is reviewed by a practising CTO or Principal Engineer before anyone hiring sees it.
An always-on chat and voice mentor for career strategy, expertise coaching, workflow-transformation advice and interview preparation. It is connected to your own assessment, so coaching is grounded in your scored strengths and gaps rather than generic advice.
Yes to both. AI-First Match is built in Berlin and GDPR-native by design: you can request complete deletion of your profile, interview transcripts and all associated data at any time under GDPR Article 17, and export your data under Article 20. We have also validated the platform for compliance with the EU AI Act (Regulation (EU) 2024/1689), which treats AI used in hiring as high-risk: every assessment runs under human oversight (Article 14) - a CTO or Principal Engineer reviews the result and no candidate is ever rejected by AI alone - every score is transparent and traceable to verbatim interview evidence (Article 13), and assessment records stay auditable (Article 12). European data protection and AI governance sit at the core of the platform.
Every profile and every AI assessment and evaluation is reviewed by our CTO & Principal Engineer Network - agentic AI builders with strong software engineering backgrounds. They ship agent systems themselves, so they know the difference between someone who has genuinely rebuilt a workflow around AI and someone who has learned the vocabulary. The AI does the structured scoring at scale; the network makes sure the result is one a hiring team can act on.
A traditional interview depends on who happened to run it, what they thought to ask, and how they remembered it afterwards. AI-First Match runs the same structured rubric for every candidate, scores each area against fixed anchors, and requires a verbatim transcript quote behind every score - so two experts are compared on the same evidence standard, and the reasoning stays auditable after the call. A practising CTO or Principal Engineer then reviews the finished assessment, which a fully automated screen never gets. It also collapses the funnel: instead of a 30-60 minute recruiter screen plus a separate 45-90 minute technical interview, one 45-60 minute call does both - and your engineers meet only the reviewed shortlist.
AI-First Match is currently invitation-based - join the waitlist and we'll be in touch with early-access details. Because every profile is reviewed by our CTO & Principal Engineer Network, we onboard deliberately rather than all at once. The platform is built in Berlin, Germany, with a GDPR-native architecture, and is backed by Nvidia, ElevenLabs and Sentry.
Whether you're advancing your AI-first career or building a team of product engineers powered by agentic AI, AI-First Match gives you the intelligence layer your career or hiring process needs.