Vikram Guntuka

Enterprise HR teams lacked a unified AI-powered platform to automate the complete recruitment lifecycle from job creation to employee onboarding.

Designed a multi-tenant SaaS platform with AI-powered ATS, resume parsing, candidate ranking, and an intelligent interview engine with adaptive question generation.

FastAPI microservices + LangChain orchestration + PostgreSQL multi-tenant DB + React frontend. n8n handles workflow automation. Claude and OpenAI power LLM features.

Automated 80% of recruitment screening workflows, reducing time-to-hire by 60% for enterprise clients.

Traditional ATS platforms required extensive manual effort for screening, scheduling, and candidate follow-up.

Built an Agentic AI recruitment platform where intelligent AI agents autonomously manage the entire ATS workflow.

LangGraph-based multi-agent architecture with LangChain orchestration, FastAPI backend, PostgreSQL, and Docker deployment.

Reduced recruiter workload by 70% through full automation of screening, scheduling, and follow-up workflows.

Healthcare professionals needed instant access to domain-specific knowledge buried across thousands of clinical documents.

Built a production-grade RAG platform with intelligent document ingestion, hybrid search, and LLM-powered question answering.

LangChain + Elasticsearch + pgvector for hybrid retrieval. FastAPI microservices with Docker deployment. Llama 3 for inference.

Reduced clinical document search time by 85%, enabling instant knowledge retrieval across 50K+ documents.

Off-the-shelf LLMs lacked domain-specific accuracy for specialized healthcare and HR use cases.

Fine-tuned Llama 2, Llama 3, and Mistral using LoRA, QLoRA, and PEFT for domain-specific performance.

Hugging Face Transformers + BitsAndBytes quantization + LoRA/QLoRA adapters. GPU-optimized training pipeline.

Achieved 15-25% improvement in domain-specific task accuracy compared to base models.

Enterprise document processing required manual extraction and classification, leading to slow turnaround and errors.

Built an end-to-end document intelligence solution combining OCR, NLP, and semantic search for automated document understanding.

OpenCV + Tesseract for OCR, LangChain for document parsing, pgvector for semantic search, FastAPI backend.

Processed 10K+ documents monthly with 95% extraction accuracy, reducing manual effort by 90%.

Logistics operations lacked real-time ETA prediction integrated with visual intelligence for package handling.

Developed a multi-agent architecture combining ETA prediction with Llama 3.2 Vision for multimodal intelligence.

LangGraph multi-agent system with Llama 3.2 Vision for image understanding. FastAPI + Docker deployment.

Improved ETA prediction accuracy by 30% through multi-modal context integration.

Database teams spent hours manually optimizing slow SQL queries in production analytics workloads.

Built an AI-assisted SQL optimization platform that analyzes, rewrites, and optimizes queries automatically.

LangChain + OpenAI for query analysis, Python for SQL parsing, n8n for workflow automation and feedback loops.

Reduced average query execution time by 40% across analytics workloads.

Social media platforms needed automated content moderation to detect and filter offensive language at scale.

Fine-tuned a BERT-based NLP model for multi-class offensive language detection with automated filtering.

Hugging Face Transformers + BERT fine-tuning pipeline. Flask API for real-time inference.

Achieved 94% F1-score on offensive language detection, deployed in production moderation pipelines.