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AI Engineer with 2+ years in Generative AI, NLP & RAG Architectures
Data Scientist & AI/ML Engineer with 2 years of experience building enterprise-grade AI solutions using Generative AI, NLP, RAG architectures, and agentic workflows. Expertise in Python, FastAPI, LangChain, LangGraph, Agno, vector databases, and modern LLM ecosystems. Developed AI-powered enterprise assistants and autonomous support copilots delivering source-cited responses, contextual reasoning, and real-time conversational support for employee operations, ServiceNow workflows, and knowledge management. Experienced across the full AI development lifecycle — document ingestion, embeddings, semantic retrieval, prompt engineering, backend APIs, agentic orchestration. Passionate about designing reliable AI systems with guardrails, contextual accuracy, and scalable architectures that improve operational efficiency.
JNTUH College of Engineering
Bachelor of Technology (B.Tech) · Computer Science
August 1, 2020 – June 30, 2024
Kaptius - We Make Workflow
Data Scientist (GenAI Developer) | ServiceNow AI Engineer
July 1, 2024 – Present
Hyderābād, Telangana, India
DeskGenie - Customer Support Copilot
January 1, 2026 – Present
Built an AI-powered ServiceNow support copilot using RAG pipelines and Agno agentic workflows, assisting support teams with intelligent case analysis, contextual retrieval, resolution recommendations, and operational guidance. Engineered FastAPI backend services with ServiceNow Workspace and chat interface integrations for real-time conversational AI support, and integrated Google Gemini for conversational reasoning, summarization, and grounded response generation, reducing manual troubleshooting effort. Built semantic retrieval pipelines using PostgreSQL pgvector with BGE-small-en embeddings across cases, KB articles, and resolution notes, combined with Cross-Encoder reranking to improve contextual relevance and optimize grounded response quality before passing context to Gemini. Engineered Agno agentic workflows with Composio tools for case summarization, user intent analysis, resolution recommendations, historical case insights, and contextual knowledge article retrieval based on dynamic user requirements. Developed webhook-based sync pipelines using ServiceNow Business Rules, Scripted REST APIs, and FastAPI to auto-ingest resolved cases and KB articles into PostgreSQL, with automated CSAT score estimation and KB article generation for unresolved knowledge gaps. Designed human-in-the-loop governance workflows with ServiceNow feedback tracking and admin review pipelines for negative feedback trends, and utilized Redis for low-latency session memory and multi-turn conversation continuity. Implemented enterprise AI guardrails including prompt injection prevention, PII masking, grounded validation, confidence scoring, hallucination reduction, and audit logging, with interactive dashboards visualizing CSAT scores, SLA resolution times, feedback ratios, and team-level performance metrics.
OrgMind - Enterprise Employee Intelligence Assistant
June 1, 2025 – November 1, 2025
Built a multi-agent enterprise HR assistant using LangChain, LangGraph, and dual RAG workflows, delivering source-cited responses for company policies, onboarding, benefits, and organizational knowledge. Engineered document ingestion pipelines for PDF and OCR-based content extraction using PyMuPDF, OCR technologies, and LangChain text splitters with Qdrant and Sentence Transformer embeddings, achieving 90% faster retrieval through metadata-filtered semantic search. Designed LangGraph agentic workflows for intent detection, multi-step retrieval, validation, and tool orchestration, reducing irrelevant responses by 90% across employee queries. Built FastAPI backend with JWT authentication and RBAC, integrated Google Gemini for reasoning, OCR, and summarization, and developed a natural language-to-SQL agent for structured employee record queries. Implemented enterprise AI guardrails including prompt injection prevention, confidence scoring, sensitive data masking, and audit logging, with MongoDB for persistent multi-turn conversational memory. Developed React + Tailwind CSS frontend with login, chat, feedback, and audit log components for seamless cross-platform access.
Cultural Fit Analysis
The candidate's projects demonstrate a strong focus on building practical, impactful AI solutions for enterprise environments, such as customer support copilots and HR assistants. This indicates a results-oriented mindset and an ability to translate complex AI concepts into tangible business value. The breadth of technologies used and the full lifecycle involvement from document ingestion to guardrail implementation suggest adaptability and a comprehensive approach to problem-solving. The candidate's experience aligns well with a culture that values innovation, practical application of AI, and robust system design.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, critical thinking, and a structured approach to developing complex AI systems. The emphasis on reducing manual effort, improving efficiency, and designing human-in-the-loop governance workflows suggests a strong operational fit and an understanding of real-world deployment challenges. While direct evidence of communication and collaboration soft skills is not explicitly detailed in the project descriptions, the successful delivery of complex enterprise solutions implies effective teamwork and communication.