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AI Engineer with 9+ years in GenAI, Agentic AI & AI Governance
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AI Engineer with overall 8+ years of experience in IT industry designing enterprise AI systems, including GenAI applications, Agentic AI workflows, Retrieval-Augmented Generation (RAG), AI Evaluation, and Responsible AI Governance. Experienced in architecting production-ready AI platforms using LangGraph, AWS Bedrock, Python, and serverless cloud technologies. Built AI systems with explainability, observability, security, evaluation, and governance at their core, enabling scalable and enterprise-grade deployments. Passionate about designing AI solutions that balance business value, technical excellence, and responsible AI practices.
NIT Warangal
PG Diploma · Artificial Intelligence & Machine Learning
N/A – Present
Freelance & Independent Projects
AI / GenAI Engineer
May 1, 2023 – Present
India
GE Healthcare
Software QA & Automation Engineer
January 1, 2013 – December 31, 2019
India
Infosys
Software QA & Automation Engineer
January 1, 2013 – December 31, 2019
India
Caterpillar
Software QA & Automation Engineer
January 1, 2013 – December 31, 2019
India
AI Resume Intelligence Platform
May 1, 2023 – June 1, 2026
Designed a governance-aware AI platform for intelligent resume analysis, semantic skill matching, candidate evaluation, and personalized career recommendations using Agentic AI workflows. Designed end-to-end LangGraph workflow orchestrating resume parsing, semantic retrieval, governance, evaluation, scoring, and recommendation. Developed semantic skill matching engine improving resume-to-job alignment beyond traditional ATS keyword matching. Built explainable candidate scoring framework combining semantic similarity, experience alignment, and skill relevance. Integrated AI evaluation framework using LangSmith, RAGAS, and DeepEval for workflow validation and quality assessment. Implemented governance-aware architecture supporting explainability, validation checkpoints, and structured AI decision making.
BroBuddy | Enterprise Conversational AI Platform
May 1, 2023 – June 1, 2026
Designed a governance-first conversational AI platform using AWS Bedrock and serverless architecture with strong emphasis on Responsible AI, observability, and operational efficiency. Developed enterprise conversational AI solution using AWS Bedrock, Lambda, and API Gateway. Designed deterministic routing workflows to optimize LLM utilization and improve operational efficiency. Implemented Prompt Firewall, Privacy Guard, validation pipelines, and audit logging for Responsible AI. Built CloudWatch-based observability framework covering latency, routing decisions, safety events, and AI telemetry. Optimized AI inference workflows identifying opportunities to reduce approximately 25–35% of unnecessary LLM invocations.
KittyLit | AI-Powered Recommendation Platform
May 1, 2023 – June 1, 2026
Developed an intelligent recommendation platform leveraging semantic retrieval, Agent-based workflows, and explainable AI to deliver personalized book recommendations. Built semantic recommendation engine using FAISS vector search and embedding-based retrieval. Designed intelligent retrieval workflow combining cache, database, and retrieval orchestration. Implemented explainability layer enabling transparent recommendation reasoning. Applied governance controls to support responsible AI recommendations and reliable decision-making.
AI Security & Governance
Securiti
June 1, 2026 – Present
Building AI Applications with Amazon Bedrock
Unknown
June 1, 2026 – Present
n8n Workflow Automation
Great Learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (resume analysis, conversational AI, recommendation systems) and the breadth of skills across AI/ML, cloud, and governance indicate a strong adaptability and willingness to tackle varied challenges. The transition from traditional QA to cutting-edge GenAI demonstrates a proactive learning mindset, aligning well with innovative and fast-paced environments. The focus on Responsible AI also suggests a strong ethical and quality-driven approach.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong emphasis on operational efficiency, observability, and responsible AI practices, indicating a good fit for roles requiring robust, production-ready AI systems. Their background in QA and automation suggests a detail-oriented approach and a focus on system reliability and quality.