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AI Engineer with 2+ years in LLM applications & cloud-native AI services.
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AI Engineer with 1.5 years of experience building production-grade LLM applications, agentic workflows, and cloud-native AI services. Rapidly promoted from Intern to Data Engineer (L2) within 8 months at RR Donnelley and recognised with the Best Performance Award (R&R 2025). Expert in LLM orchestration, prompt engineering, Retrieval-Augmented Generation (RAG), and AWS Bedrock integration. Consistently converts unstructured inputs into client-ready outputs (PPT, Excel, Word) and ships scalable, enterprise-grade pipelines.
Vellore Institute of Technology
M.Sc. · Data Science
N/A – June 30, 2025
Loyola College
B.Sc. · Mathematics
N/A – June 30, 2022
RR Donnelley
Data Engineer
February 1, 2025 – Present
India
RR Donnelley
Data Analyst Intern
February 1, 2025 – October 1, 2025
India
SCOOP - Research Automation Platform (3-Version Evolution)
February 1, 2025 – June 1, 2026
End-to-end AI platform that automates analyst research workflows, producing structured insights and client-ready multi-format reports (PPT, Excel, Word) through LLM orchestration. Evolved iteratively from a static proof-of-concept to a fully configurable, cloud-native system. • Version 1 — Static Pipeline: Built the foundational platform with hardcoded prompts and a single fixed output format. Established the core LLM orchestration architecture and validated end-to-end data flow from raw inputs to a structured report, serving as the proof-of-concept for stakeholder buy-in. • Version 2 — Configurable & Multi-Format Output: Re-architected the platform for full developer-assisted configurability and rich output flexibility: • Prompt Configuration: Replaced hardcoded prompts with a developer-guided prompt-builder, allowing users to define their own query intent; the system provides structure, validation, and context enrichment. • Content Placeholders: Introduced a placeholder selection system for PowerPoint output — users pick which content populates each slide element (title, body, footer, etc.) without touching template files. • Multi-Format Output Engine: Users choose output content type per section — tables, bullet lists, charts, or shapes — and the system dynamically generates the appropriate element in PPT, Excel, or Word. • Modular Architecture: Designed reusable pipeline components, enabling rapid rollout of new output formats and report templates with minimal code changes. • Version 3 — Cloud-Native AWS Migration (In Progress): Migrating the full platform to a scalable, serverless cloud architecture: • Replacing local LLM inference with Amazon Bedrock (Anthropic Claude models) for managed, scalable LLM serving. • Moving document storage and report artefacts to AWS S3; decoupling pipeline stages using AWS Lambda for event-driven execution. • Designing cloud-native prompt configuration and retrieval services to support concurrent multi-user workflows at enterprise scale.
DocuLenz - Intelligent Lease Document QA
February 1, 2025 – October 1, 2025
• Developed an OCR and retrieval pipeline using MiniCPM for text extraction from scanned and digital PDFs with bounding-box precision. • Integrated an LLM-driven Q&A layer over chunk-based retrieval, enabling natural-language queries against complex lease documents. • Implemented page-level metadata storage for traceable, citation-backed responses.
Python 101 for Data Science
IBM CognitiveClass
June 1, 2026 – Present
Best Performance Award (R&R 2025)
RR Donnelley
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's experience with diverse projects (research automation, document QA, various content generators) and technologies (multiple LLMs, cloud platforms, vector databases) indicates adaptability and a broad skill set, aligning well with dynamic AI engineering environments. Their involvement in cloud migration and designing modular microservices suggests a forward-thinking approach and a good fit for organizations embracing modern, scalable architectures. The recognition for high-impact AI solutions and rapid promotion points to a strong drive for excellence and contribution.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through iterative platform evolution and re-architecture. Their rapid promotion and performance award indicate a proactive, results-oriented work attitude and strong delivery capabilities. The ability to lead migrations and design scalable microservices suggests leadership potential and a strategic mindset. The focus on end-user configurability and multi-format outputs highlights a user-centric approach to development.