AI SaaS / Career Technology Platform
AI Job Search Agent — Explainable Career Opportunity Matching Platform
An explainable AI job-search platform that converts natural-language career goals and candidate profiles into ranked job opportunities, grounded match explanations and an organized application pipeline.
- Role
- AI Product Manager & AI Solution Architect
- Project type
- AI SaaS / Career Technology Platform
- Status
- Functional MVP / Validation Stage
- Organization
- Personal project
- Timeline
- 2026-07-02 — Present
- Team
- Independent product-led project developed using AI-assisted engineering tools. I owned the complete product lifecycle, including problem definition, product strategy, requirements, AI solution design, user experience, system architecture, implementation decisions, testing, deployment planning and portfolio documentation. The solution includes a Next.js frontend, FastAPI backend, PostgreSQL database, Redis caching, AI and embedding services, external job-provider integrations and automated CI/CD workflows.
- Confidentiality
- public
- Published
- published
Product walkthrough
Selected screens
Confidentiality note
This is an independent portfolio project based on publicly available technologies, APIs, sample data and a public GitHub repository. It does not contain confidential employer information, proprietary source code, private customer data or internal business metrics. Any example profiles, job searches, outcomes or product assumptions shown in this case study are illustrative or generalized for demonstration purposes.
Project overview
AI Job Search Agent is a full-stack career decision-support platform designed to reduce the time and effort required to find relevant job opportunities.
Users can describe the work they want in natural language, such as “remote senior AI product manager roles in APAC.” The system extracts role, skills, seniority, location and remote-work preferences, then ranks available jobs using semantic similarity, skill overlap, keyword relevance and location compatibility.
Candidates can create a reusable profile containing their skills, experience, preferred roles, location preferences and resume text. The platform uses this context to generate personalized recommendations and explains why each role matches through a visible match score and signal-level breakdown.
The product also supports the workflow after discovery by allowing candidates to save jobs, open the original listing, mark applications as applied and track progress through saved, applied, interview and rejected statuses.
Business problem
Job seekers have access to thousands of job listings, but they still perform most of the difficult work manually. They must repeatedly configure filters, search across multiple platforms, interpret inconsistent job titles, compare long job descriptions with their experience and decide which opportunities deserve attention. Traditional keyword search can miss relevant roles when employers use different titles. It can also return weak matches simply because a job description contains the same keywords. After finding a suitable job, candidates often manage saved positions and application progress across browser bookmarks, spreadsheets, emails and separate job platforms. The core problem is not a lack of available jobs. It is the lack of an efficient, personalized and transparent way to identify the opportunities most relevant to each candidate.
Product objective
Build an intelligent job-search workspace that understands candidate intent, identifies relevant opportunities, explains every recommendation and supports the workflow from discovery to application tracking. The MVP was designed to validate whether natural-language search, candidate profile context and explainable hybrid ranking could help users focus on a smaller number of stronger job opportunities. The product also aimed to establish a scalable technical foundation for future capabilities such as job alerts, skill-gap analysis, resume tailoring and interview preparation.
Product approach
I followed an evidence-gated product lifecycle consisting of four phases. Conceive: I defined the customer problem, target users, Jobs To Be Done, product vision, value proposition and critical assumptions. Plan: I translated the concept into an MVP-focused PRD covering user stories, functional requirements, non-functional requirements, prioritization, AI strategy and release criteria. Develop: I created the full-stack MVP and validated that the complete workflow was technically feasible—from natural-language search and personalized ranking to explanation and application tracking. Launch planning: I defined the analytics event taxonomy, AI-quality benchmark, controlled-pilot design, success metrics and security requirements needed before using the product with real candidate resume data. Throughout the project, I clearly separated implemented capabilities from assumptions, proposed targets and unvalidated business outcomes.
Solution summary
The solution is a full-stack web platform combining AI-assisted query understanding, semantic retrieval, deterministic ranking and workflow management. Candidates enter a natural-language search query. The backend extracts structured filters such as role, skills, seniority, location and remote preference. An embedding represents the query or candidate profile and is compared with normalized job embeddings. The platform calculates a hybrid match score using semantic similarity, skill overlap, keyword relevance and location compatibility. Each job result includes a match percentage, matched skills, a concise explanation and a detailed score breakdown. A reusable candidate profile supports personalized recommendations using skills, experience, preferred roles, location preference and resume text. The ingestion layer normalizes jobs from Remotive, Adzuna, Greenhouse, Lever and controlled JSON imports. PostgreSQL stores jobs, profiles and application records, while Redis caches search and recommendation results. The frontend provides search, job details, candidate profile, personalized recommendations, saved jobs and application tracking. The backend is implemented with FastAPI, while automated workflows validate the backend API and frontend production build.
Target users
- Technology professionals actively searching for relevant career opportunities.
- Mid-level and senior product managers, software engineers, data professionals, AI specialists and solution architects.
- Career switchers who have transferable experience but may not know the exact job titles used by employers.
- Remote and regional candidates who need location and remote-work preferences considered during matching.
- High-volume applicants who need one workspace to discover, save and track opportunities.
Selected product decisions
Positioned the product as a candidate decision-support workspace rather than another general job board
Prioritized relevance and decision confidence over the volume of displayed jobs
Used natural-language search instead of requiring users to configure complex filters
Displayed extracted filters so users could inspect how their query was interpreted
Selected hybrid ranking rather than keyword-only or LLM-only ranking
Assigned the numeric match score to deterministic logic rather than generative AI
Used semantic similarity, skill match, keyword match and location match as ranking signals
Separated ranking calculation from natural-language explanation generation
Exposed score components through a visible “Why this match?” section
Added deterministic parsing and explanation fallbacks for unavailable AI services
Created a shared internal job model for normalizing different provider formats
Included candidate profiles and recommendations in the MVP to support persistent personalization
Included application tracking so the product delivered a complete workflow rather than an isolated AI feature
Preserved links to original job sources instead of replacing the provider application process
Deferred automatic job applications because of trust, privacy and quality risks
Required authentication, analytics and privacy controls before a real external pilot
Avoided presenting proposed metrics or targets as achieved product outcomes
Outcomes
Qualitative
Delivered a functional end-to-end AI job-search MVP
VerifiedQualitative
Implemented natural-language search and personalized job recommendations
VerifiedQualitative
Designed an explainable hybrid ranking model with visible match signals
VerifiedQualitative
Integrated and normalized jobs from multiple external providers
VerifiedQualitative
Connected job discovery with saving and application-status tracking
VerifiedQualitative
Added PostgreSQL persistence, Redis caching and deterministic AI fallbacks
VerifiedQualitative
Deployed the full-stack product with automated CI validation
VerifiedQualitative
Produced essential product, AI strategy, architecture and pilot-planning documentation
VerifiedQualitative
Validated technical feasibility; customer adoption and product-market fit remain unproven
VerifiedChallenges
- Normalizing inconsistent job data returned by different external providers
- Preventing duplicate listings across provider IDs, URLs and similar job records
- Balancing semantic relevance with explicit skills, keywords, seniority and location requirements
- Making AI-generated recommendations understandable and trustworthy
- Preventing the language model from inventing unsupported match explanations
- Maintaining useful search behavior when the external AI service is unavailable
- Handling job providers that require credentials or become temporarily unavailable
- Working with incomplete job descriptions, missing salaries and inconsistent seniority labels
- Protecting sensitive candidate profile and resume information
- Designing an MVP that delivered a complete workflow without becoming too broad
- Separating technical feasibility from actual customer and business validation
- Avoiding misleading success metrics before completing a controlled user pilot
Lessons learned
- AI alone is not the product; it must support a meaningful end-to-end user workflow
- Natural-language interaction becomes more trustworthy when the extracted interpretation is visible
- Semantic similarity improves discovery but should not be the only ranking signal
- Numeric ranking should remain deterministic, measurable and version-controlled
- Generative AI is better suited to explaining calculated evidence than secretly making every decision
- Explainability supports both user trust and product-team debugging
- Fallback behavior is a product requirement, not only an engineering concern
- External data quality can affect recommendation quality as much as the AI model
- Candidate profiles can improve personalization but create privacy and onboarding trade-offs
- Connecting discovery with application tracking creates more lasting value than an isolated search feature
- Authentication and analytics should be implemented before conducting a real pilot
- A functional MVP proves feasibility, but it does not prove customer value or product-market fit
- Future AI automation should be introduced only after user trust and quality have been validated
- Product documentation is strongest when actual evidence is separated from assumptions and proposed targets
Project artifacts
Selected documents and supporting product work.
All
Document
Essential Product Documents
A polished product management portfolio document covering the AI Job Search Agent’s full lifecycle, including the Product Concept Document, PRD, AI strategy, system architecture, requirements traceability, launch-readiness assessment and pilot measurement plan.