Stop guessing which software role you're ready for.
Talent2Role Software & Development reads your projects, stack and shipped work, maps them
against 14 software-development and AI/ML work roles — frontend to firmware, data scientist to
LLM application developer — and shows you exactly which competencies you already evidence, which
ones you're missing, and the shortest credible path to the role you want.
Free to start · No credit card · Your profile is stored securely in your account and follows you across devices
software.talent2role.com/app#gap
83%Role readiness
49Competencies matched
55Priority gaps
Full-Stack Developer83%
Backend Developer78%
Frontend Developer71%
MLOps Engineer44%
The journey
Résumé to offer — one flowing process
Watch how each stage feeds the next, and what it changes for you.
1
CaptureFree
2
MapFree
3
AnalysePro
4
PlanPro
5
ProvePro
Capture
The problem
Software has a dozen standards. Careers don't use any of them.
Two "Software Engineer" jobs at two companies can share almost nothing, and "AI Engineer" means
five different things. SFIA, SWEBOK, O*NET, ESCO and the CS curricula each describe part of the
work; none of them is the vocabulary a hiring manager or a career changer actually uses. The SDWF
and AIWF frameworks synthesise them into one NICE-style graph: 14 work roles, 22 competency
areas, 406 statements with stable IDs.
Careers are still assessed from a keyword list
A recruiter's desk sees a résumé, a list of frameworks and a job title. It cannot see what the person can actually build — or how far they are from the role they want next.
14work roles
22competency areas
406TKS statements
6role categories
Job titles carry no signal
"Software Engineer" might mean React components, Kubernetes platforms or firmware drivers; "AI Engineer" might mean prompt design or training pipelines. You cannot tell whether you qualify from the title, and neither can the recruiter.
What breaks today
"Learn some ML" isn't a plan
Which knowledge statements, in what order, costing how many weeks, moving your readiness by how much? Career advice stops exactly where the useful detail starts.
What breaks today
Keyword matching misses competence
An ATS compares strings. It cannot tell that "shipped the checkout service" and "designed, tested and deployed a production API" describe the same SDWF competencies.
What breaks today
Code to model
One competency graph from frontend to foundation model.
The same graph describes the web team, the mobile and firmware engineers, the data scientists and the people shipping LLM applications. That is what makes the move from developer to AI engineer plannable — and measurable.
Web & application development
Frontend, backend and full-stack development — APIs, data, UI, testing, CI/CD and secure coding.
WA — 3 roles
Platform & systems, low-code
Mobile, embedded/firmware and game development, plus low-code and no-code building.
PS · DD — 4 roles
Machine learning & data science
ML engineering, MLOps, computer vision, NLP and data science — from statistics to production.
ML · DS — 5 roles
Generative & applied AI
Prompt and context engineering and LLM application development — retrieval, agents, evals and safety.
GA — 2 roles
The platform
A complete career application. One competency graph.
Every module runs off the same SDWF + AIWF ontology and your centrally stored profile — an insight in one module is immediately usable in the next, on any device.
Dashboard
Readiness score against your target role, momentum over eight weeks, priority gaps, and the single next action worth taking.
Profile
Projects shipped, stack and tooling, certifications, open-source work and self-assessed competencies — the evidence base every score is derived from.
Process
A guided five-stage journey — capture, map, analyse, plan, prove. Your AI Career Counsellor reads your résumé for skills, knowledge and capabilities; a rule engine plus the counsellor map them to framework statements.
Resume Gap Analyzer
The flagship. Paste your résumé or GitHub profile summary and get a statement-level match against all 14 work roles, with every missing TKS statement listed by ID — and a counsellor-written summary of what matters most.
Profile Compare Pro
Paste any other profile — a role model, a colleague, the person who got the job — and see the exact statements between you, an AI gap summary and a phased plan to close it.
Role Explorer
Browse all 14 roles across the six SDWF and AIWF categories, read the exact knowledge and skill statements each demands, and see which roles are adjacent to yours.
Learning Path
Your gaps grouped into SDWF and AIWF competency areas and ordered by readiness gain per hour — biggest impact per hour invested, first.
Job Match
Paste any engineering or AI job advert. It gets mapped to its closest SDWF or AIWF role, scored against your profile, and the gaps that would sink the interview are flagged first.
Badges & Certificates Free cert
Earn alignment badges and a QR-verifiable Certificate of Mapped Skills — add them to LinkedIn in one click. Alignment tiers unlock with Professional.
Decision Studio Pro
Fourteen models from The Decision Book — SWOT, Johari Window, Belbin, Hard Choice, Stop Rule and more — drawn as charts from your own framework data, for interview prep and choosing between offers.
Role-Fit Report Pro
A 12-section career readiness report — analytics, SWOT, cert ROI, 13-week timetable, interview prep — printable and QR-verifiable.
Scenario Answers Pro
Real engineering scenarios — a bad deploy, a flaky suite, a critical CVE, a drifting model, a hallucinating chatbot — mapped to the knowledge, skills and abilities they demonstrate, with exact guidance on using each one in interview answers.
Soft Skills Pro
The career-deciding soft skills of engineering — code review, systems thinking, debugging under pressure, product sense, responsible AI — what each covers, why it matters, and concrete ways to improve.
Job Keywords Pro
Titles, terms and boolean search strings tuned to your target role — copy them straight into LinkedIn, Indeed, Bayt or Google.
Portal Profile Pack Pro
Every profile field for LinkedIn and the job portals — headline, about, experience, skills — generated from your evidence, ready to copy.
Resources & Google Notebook Pro
Curated videos, books and references per learning theme — plus a one-click study pack for Google Notebook (NotebookLM) with quizzes and audio overviews.
Advisory
For engineering leaders and bootcamps: SDWF/AIWF capability assessment, hiring advisory and curriculum design — human expertise on top of the platform's output.
How it works
Five stages, resume to offer
The same process runs inside the platform as a guided journey, with your progress tracked at every stage.
1
Capture
Sign in and bring your evidence — résumé, projects shipped, stack, certifications, open-source work. Detail is what the engine has to work with.
2
Map
Your evidence is reduced to competency signal and matched against 406 SDWF and AIWF TKS statements.
3
Analyse
Scored against all 14 work roles. Readiness, matched statements, and every gap listed by ID.
4
Plan
Gaps become a sequenced curriculum with effort estimates and the readiness impact of each theme.
5
Prove
Close gaps, track readiness climbing, and score real job adverts before you apply.
The framework
Why a NICE-style framework for software, and what that actually buys you
Software has no single workforce standard, so Quantum Task AI built one the way NIST built NICE:
the Software Development Workforce Framework (SDWF) and the AI/ML Workforce Framework (AIWF)
synthesise SFIA 9, the SWEBOK Guide v4, O*NET, ESCO, the e-Competence Framework and the CS2023
curricula into categories, work roles, competency areas and task, knowledge and skill statements
with stable IDs — the same shape the engine scores for every other vertical.
What a competency statement looks like
SDW-K1001Knowledge of software engineering principles and the software development life cycle (SDLC).
SDW-S1012Skill in configuring and maintaining CI/CD pipelines.
AIW-S1014Skill in monitoring production AI systems for drift, degradation, and anomalies.
Every statement has a stable ID. That is what makes gap analysis possible: your résumé either
evidences AIW-S1014 or it doesn't, and
if it doesn't, that is a concrete thing to go and learn — not a vague instruction to
"get some MLOps experience".
Who it's for
Four people, one competency graph
The same engine answers a different question depending on who is asking it.
Working developers
You ship code and want the next role — senior, full-stack, platform, or a move into ML and AI engineering.
Which roles am I already close to?
What exactly is missing?
How long to close it?
Career switchers & bootcamp grads
Coming from support, data analysis, science, a bootcamp or a low-code background, and unsure where you actually land.
Which engineering roles fit what I've done?
What's the shortest bridge?
Which certs actually matter?
Engineering managers & CTOs
You have to evidence team capability, plan AI upskilling and justify a training budget with something defensible.
Where is the team thin?
What should we hire vs. train?
Prove coverage against a standard
Bootcamps & universities
Programmes that need curriculum mapped to the roles employers actually hire for — including the AI roles that did not exist three years ago.
Map a syllabus to SDWF/AIWF roles
Show learners their gap closing
Evidence outcomes to employers
Pricing
Start free. Pay when it's working.
Individuals get the full analysis engine at no cost. Engineering teams and training providers pay for scale, evidence and advisory.
Explorer
Free
For anyone finding their next engineering or AI role.
