AI Development Projects in New DelhiAI Development Projects in New DelhiAI Resume Screening | Candidate Ranking System | AI HR Recruiter | ATS CV/Resume Optimization
𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄
Recruiters often spend hours manually reviewing resumes, comparing candidate qualifications, and identifying the best fit for open positions. To address this challenge, I developed an AI-powered Resume Screening and Candidate Ranking Platform that automates candidate evaluation, improves hiring efficiency, and reduces recruitment time.
𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲
Traditional recruitment processes involve reviewing hundreds of resumes for a single position. This manual approach is time-consuming, inconsistent, and often results in qualified candidates being overlooked. Recruiters needed a solution capable of quickly analyzing resumes, matching them against job requirements, and generating reliable candidate rankings.
𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻
I built an intelligent recruitment platform that leverages Artificial Intelligence and Natural Language Processing (NLP) to automate resume analysis and candidate assessment.
𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
- ATS-compatible resume parsing for PDF and DOCX files
- Automated extraction of skills, experience, education, certifications, and contact information
- AI candidate matching based on job descriptions
- Intelligent candidate scoring and ranking system
- Semantic skill matching using NLP techniques
- Automated shortlist generation for recruiters
- Recruiter dashboard for managing applications and rankings
- Bulk resume processing for high-volume recruitment
- Interview recommendation system based on candidate fit
- Fair and consistent evaluation framework to reduce manual bias
𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀
The platform was designed with scalability and accuracy in mind. The workflow begins by parsing uploaded resumes and extracting structured candidate data. AI models then compare candidate profiles against job requirements, analyzing technical skills, years of experience, educational background, and industry relevance.
A ranking engine generates compatibility scores and presents candidates in order of suitability. Recruiters can review detailed scoring insights, compare applicants, and make faster hiring decisions.
𝗥𝗲𝘀𝘂𝗹𝘁𝘀
The solution significantly improved recruitment efficiency and candidate discovery.
𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀
> Reduced manual resume screening time by up to 80%
> Accelerated candidate shortlisting process
> Improved recruiter productivity and hiring speed
> Increased consistency in candidate evaluation
> Enabled processing of hundreds of resumes within minutes
> Enhanced talent identification through AI-driven matching
𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻
This AI recruitment platform transforms traditional hiring workflows by automating resume screening, ranking candidates intelligently, and helping recruiters identify top talent faster, more accurately, and at scale. I offer AI, data science, analytics, software development, cloud computing, cybersecurity, and digital transformation services for startups and enterprises.
My expertise includes machine learning, generative AI, big data analytics, business intelligence, web and mobile app development, IoT solutions, and enterprise automation.
I also provide consulting, staffing, and industry-specific solutions for healthcare, finance, retail, insurance, energy, and supply chain businesses. Built and maintained a HIPAA compliant production medical AI scribe system that uses multi-step LLM agents to extract clinical entities directly from physician-patient conversations and turn them into structured medical notes, cutting down the manual transcription work clinicians used to do after every visit.
Multi-stage clinical workflow: Documentation, coding, and review each have different logic and conditional paths, and the system needed to branch correctly between them without losing context.
Clinical accuracy: Generated notes had to be grounded in real patient history and clinical guidelines, not just plausible-sounding text.
Production reliability: As a live system handling real conversations, every agent run needed to be observable, debuggable, and monitored for cost and latency in real time.
Non-technical requirements gathering: Clinical needs had to be captured accurately from stakeholders without a technical background and translated into precise agent logic.
Approach:
Stateful agent orchestration with LangGraph
Designed LangGraph-based agent workflows with conditional branching, allowing the system to move correctly across documentation, coding, and review stages based on conversation content.
Context-grounded note generation with RAG
Built a RAG pipeline on AWS Bedrock with embeddings, so every generated note is grounded in the patient's actual history and relevant clinical guidelines rather than generic output.
Full production observability
Integrated Langfuse across all agent runs to track token usage, latency, and model KPIs, giving the team visibility into system health and cost in production, not just at build time.
Clinical stakeholder collaboration
Ran requirements sessions directly with clinical staff, converting their documentation needs into concrete agent behavior specs and validation criteria.
Results & Impact:
~40% reduction in manual transcription time for clinicians using the system.
Clinically grounded output, with notes tied to real patient history and guidelines instead of unsupported generation.
Full production observability, with token usage, latency, and model performance tracked continuously.
A workflow clinicians could trust, built through direct collaboration rather than a black-box handoff.
Provided Services & Solutions:
📌 AI Agent Development (LangGraph)
📌 RAG Pipeline Development (AWS Bedrock)
📌 LLM Observability (Langfuse)
📌 Cloud Infrastructure (AWS Lambda, S3, DynamoDB)
📌 Stakeholder Requirements Translation
📌 Production ML Systems
Tech Stack
Python · LangChain · LangGraph · AWS (Bedrock, Lambda, S3, DynamoDB) · Langfuse · TypeScript · REST APIs 
