01 · Overview
Product context and engineering scope.
The goals, operating context, and technical decisions behind this case study.
GiFTEM is a production enterprise B2B SaaS platform that replaces fragmented recruiting workflows with a unified intelligence layer for understanding jobs, sourcing candidates, evaluating fit, analyzing recruiting markets, and managing candidate outreach.
Built for Shuvel Digitech Private Limited
01
The platform transforms unstructured job descriptions into structured role taxonomy, skill ontology, required and preferred skills, related titles, tools, certifications, and searchable candidate criteria. Recruiters can then use Explore, Strict, Boolean, and filter-based search modes for both precise matching and broader talent discovery.
02
At the product level, GiFTEM combines multidimensional candidate-fit evaluation, skill-gap analysis, explainable match insights, market intelligence, personalized email outreach, conversational Voice AI, follow-up automation, transcription, sentiment analysis, and interview scheduling.
03
As a core AI and full-stack engineer, I contributed across the frontend, backend, AI-processing, search, data, asynchronous workflow, and cloud layers. My work includes job-driven sourcing, job-intelligence integration, role-aware search strategies, candidate match insights, skill-gap analysis, AI-generated recruiting playbooks, location-aware Boolean generation, recruiter automation, and production background-processing workflows.
04
I improved production reliability through structured AI outputs, schema validation, retries, fallbacks, staged generation, incremental persistence, failure-safe status handling, PostgreSQL optimization, Redis-backed processing, and Bull queues for long-running AI and search operations.
02 · Key Contributions
What I engineered across the product.
Every original contribution preserved in sequential chapters, highlighting technical execution.
As AI Engineer / Full Stack AI Engineer at Shuvel Digitech Private Limited, I engineered core capabilities across the product.
3 engineering deliverables
01
Built AI-assisted candidate sourcing workflows that transform job requirements into structured searches across job titles, locations, experience, skills, industries, companies, and security-clearance criteria.
02
Developed Strict, Explore, and Boolean sourcing experiences that balance precise mandatory filtering with broader AI-assisted candidate discovery.
03
Contributed to job-intelligence workflows that extract role taxonomy, skill ontology, required and preferred skills, related titles, tools, certifications, and structured requirements from job descriptions.
3 engineering deliverables
04
Built job-to-candidate match-analysis experiences that provide fit scores, requirement comparisons, skill coverage, skill gaps, experience alignment, and explainable recruiting insights.
05
Developed asynchronous AI-processing workflows using Redis and Bull queues to execute long-running job analysis, playbook generation, and candidate-processing tasks reliably.
06
Built a recruiting sourcing playbook that converts job descriptions, taxonomy, ontology, skills, clearance requirements, and location context into recruiter-ready candidate profiles, search strategies, Boolean strings, company targets, screening guidance, outreach templates, and submission guidance.
3 engineering deliverables
07
Implemented location-aware Boolean sourcing strategies using job locations, nearby cities, states, role aliases, skills, and clearance variations to improve candidate-search coverage.
08
Developed recruiter-facing interfaces in Next.js and React for job analysis, candidate discovery, sourcing progress, match insights, and AI-generated recruiting content.
09
Integrated frontend and backend workflows through Node.js and Express APIs, PostgreSQL models, Sequelize, Redis-backed processing, and AWS infrastructure.
1 engineering deliverables
10
Improved production AI workflows through structured outputs, schema validation, retries, fallbacks, staged generation, incremental persistence, and failure-safe status handling.
03 · Capabilities
Capabilities designed around real user needs.
The product features and system behaviors delivered by this project.
01
AI Job Intelligence
Transforms unstructured job descriptions into structured role taxonomy, skill ontology, seniority, required and preferred skills, related titles, tools, certifications, and searchable requirements.
02
Role-Aware Candidate Sourcing
Supports Explore, Strict, Boolean, semantic, and filter-based sourcing experiences that balance mandatory criteria with broader AI-assisted candidate discovery.
03
Explainable Candidate Match Intelligence
Compares job context with candidate profiles to surface fit signals, required and preferred skill coverage, experience alignment, skill gaps, and recruiter validation points.
04
AI Recruiting Sourcing Playbook
Converts job intelligence, skills, clearance requirements, and location context into candidate profiles, target titles, Boolean searches, scoring guidance, screening questions, outreach templates, and recruiter-ready sourcing strategies.
05
Unified AI Outreach
Supports personalized email and Voice AI outreach with lifecycle synchronization, follow-up automation, transcription, outcome detection, sentiment signals, communication history, and scheduling.
06
Reliable Production AI Workflows
Uses Redis and Bull queues, structured outputs, validation, retries, fallbacks, staged generation, incremental persistence, and failure-safe status handling for long-running workflows.
GiFTEM — AI-Powered Recruiting Intelligence Platform
An AI-powered recruiter decision-support system that combines job descriptions with structured job intelligence—including taxonomy, skill ontology, required and preferred skills, clearance requirements, location context, related titles, and relevant tools—to generate an actionable, job-specific candidate sourcing strategy.
Intelligence Inputs
Job descriptions
Role taxonomy and skill ontology
Required and preferred skills
Clearance requirements
Location context
Related titles and tools
Product-Ready Outputs
Recruiter role translation
Ideal candidate profile
Direct and adjacent target titles
Required and preferred skill groupings
Location and clearance strategy
Portal-specific Boolean searches
Target-company tiers
Candidate scoring guidance
False-positive profiles
Knockout questions
Recruiter notes
Personalized outreach templates
Candidate-submission templates
Final sourcing strategy
Explainability and source context
Engineering Highlights (9)
04 · System flow
How the system moves from input to outcome.
A stage-based connected process visualization preserving every workflow step.
Stage 01
Job description
Stage 02
Job intelligence
Stage 03
Sourcing strategy
Stage 04
Candidate discovery & ranking
Stage 05
Match insights
Stage 06
Personalized outreach
05 · Decisions
Constraints translated into engineering decisions.
The problems that shaped the product and the responses used to address them.
01
The constraint
Balancing mandatory recruiting criteria with broader, role-aware talent discovery
Engineering decision
Developed distinct Strict, Explore, Boolean, semantic, and filter-based sourcing paths so recruiters can move between precise constraint enforcement and adjacent-candidate exploration.
02
The constraint
Keeping long-running AI generation and candidate-processing workflows reliable
Engineering decision
Implemented Redis-backed Bull queues, schema validation, retries, repair and fallback paths, staged generation, incremental persistence, and failure-safe status handling.
03
The constraint
Making complex AI recommendations useful and explainable to recruiters
Engineering decision
Structured candidate-job comparisons around evidence, fit signals, skill coverage, missing skills, experience alignment, and explicit recruiter validation points.
06 · Technology
Technology and tools
The complete technology stack utilized in this project, organized by engineering area.
Frontend
Next.js
React
TypeScript
Backend
Node.js
Express.js
AI
OpenAI APIs
LLM Structured Generation
Semantic Search
Prompt Engineering
Search
Boolean Search
Role-Aware Filtering
Location-Aware Sourcing
Database
PostgreSQL
Sequelize
Async Processing
Redis
Bull Queues
Background Workers
Infrastructure
AWS
Engineering
REST APIs
Structured Outputs
Retries
Validation
More selected work
Continue exploring the portfolio case studies.