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AI Engineer with 1+ years in Generative AI & Agentic AI Development
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Generative AI Engineer and Agentic AI Developer with 1+ year of experience designing and deploying production-grade multi-agent AI systems for enterprise clients across banking and aviation domains. Deep expertise in LLM orchestration, Retrieval-Augmented Generation (RAG), prompt engineering, and agentic workflows using AWS Bedrock, LangGraph, LangChain, and Anthropic Claude. Proven track record of delivering measurable outcomes: 90% AHT reduction, >98% LLM extraction accuracy, and 65% FTE effort reduction at scale. Experienced in responsible AI, AI safety, guardrails, LLM evaluation, and compliance-ready deployments.
D.Y. Patil College of Engineering
Bachelor of Engineering · Artificial Intelligence and Data Science
August 1, 2021 – May 1, 2025
WNS-Vuram
Associate Technical Consultant - AI Engineer (Generative AI)
June 1, 2025 – Present
Pune, Maharashtra, India
CardOpsAI Agentic AI Multi-Agent Back-Office Automation
June 24, 2026 – Present
Architected a production-grade agentic AI system using AWS Bedrock multi-agent orchestration (Supervisor-Agent pattern) comprising Split Request, Extraction, Verification, Action, and Evaluator agents automating high-volume card operations (limit increase, address change, card block, replacement) end-to-end for a banking enterprise client. Engineered multimodal LLM extraction pipeline with >98% accuracy on unstructured emails and documents using Anthropic Claude 4.5/3.7, integrated via secure REST to core banking and card-processor APIs with full business-rule validation. Implemented Human-in-the-Loop (HITL) via Appian with an Evaluator agent enforcing compliance checks and auto-initiating re-runs on policy violations delivering SLA-compliant, fully auditable responses with end-to-end traceability. Achieved 90% AHT reduction (30 min → <3 min), 30-50% TAT improvement, 40-60% self-service deflection, and ~65% FTE effort reduction through queue-based serverless execution with dead-letter handling.
Agent CentralAI AI Monitoring, Evaluation & Governance Platform
June 24, 2026 – Present
Built a near real-time AI observability and governance platform for production AWS Bedrock agents across the full lifecycle (registration, testing, deployment, operations) — aggregating 15+ LLM evaluation metrics: Hallucination Risk, Prompt Injection, PII Detection, Context Adherence, Toxicity, Tool Selection Quality, and Completeness. Engineered automated responsible AI scanning for injection/leakage risks and guardrail optimization across action group Lambdas and knowledge bases, with actionable remediation and inclusiveness/privacy/fairness scoring. Delivered A/B testing for safe agent version comparison pre-rollout, cost analytics with anomaly alerts, CloudFormation IaC deployment, and a unified compliance dashboard — forming a single system of record for 365-degree agent governance.
Flight Disruption Management (FDM) AI System
June 24, 2026 – Present
Designed a production multi-agent conversational AI for WestJet Airlines using LangGraph and Claude 3.5 (Supervisor-Agent pattern) — automating flight cancellation and rebooking workflows with NLP-based intent detection and PNR extraction across multi-turn dialogues. Implemented RAG using FAISS vector store and Azure OpenAI embeddings to validate requests against airline SOPs in real time, with dynamic penalty calculation and complex edge-case handling (departed flights, multi-passenger bookings).
VybeStudio AI-Powered Mockup Generation System
June 24, 2026 – Present
Architected a 6-node LangGraph agentic workflow (input processing, story analysis, architecture planning, page generation, screenshot capture, validation) for automated UI mockup generation from textual requirements — reducing design time by 65% and delivering 120+ mockups in a distributed execution environment. Implemented computer vision-based consistency validation with automated screenshot comparison achieving 95% inconsistency detection; designed shared global state caching for cross-page contextual awareness, improving output consistency by 40%.
AWS Certified AI Practitioner
AWS
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases diverse applications of AI in banking, aviation, and UI generation, indicating adaptability and a broad interest in solving real-world problems across different domains. Their experience with multi-agent systems and collaborative tools (e.g., Appian for HITL) suggests a team-oriented approach. The focus on responsible AI and governance aligns with a culture that values ethical development and robust operational practices. The candidate's proactive approach to certifications and continuous learning also indicates a strong cultural fit for an innovative and growth-oriented environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex multi-agent system designs and a clear understanding of operational metrics (AHT, TAT, FTE reduction). Their experience with Human-in-the-Loop (HITL) systems and compliance checks indicates an appreciation for robust, auditable, and production-ready solutions. The emphasis on responsible AI and governance suggests a proactive approach to ethical and safe AI deployment, which aligns well with modern operational best practices.