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AI Research Engineer with less than a year in Artificial Intelligence and Data Science
Highly motivated and results-oriented AI Research Intern with a strong foundation in Artificial Intelligence and Data Science, currently pursuing a B.Tech. Proven ability to build production-grade Python automation pipelines, engineer API integration layers, and implement robust security and data integrity measures. Demonstrated expertise in full-stack development, machine learning, and cloud infrastructure through impactful projects and hackathon achievements. Recognized for academic excellence with multiple scholarships.
Shiv Nadar University Chennai
B.Tech · Artificial Intelligence and Data Science
August 1, 2023 – June 30, 2027
VidyaGyan School
Secondary & Higher Secondary · PCM (CBSE)
June 1, 2016 – May 31, 2023
Amazon
Amazon ML Summer School Trainee
August 1, 2025 – Present
India
Expelee
AI Research Intern
May 1, 2025 – July 1, 2025
Dubai, Dubai, United Arab Emirates
QRaftAI
June 18, 2026 – Present
Designed a non-blocking job pipeline - REST endpoint returns a job ID immediately, BullMQ worker processes AI generation in the background, results stream to the client over WebSocket at discrete progress milestones. Built automatic Redis/in-memory queue fallback - system detects Redis availability on boot and swaps queue backends without code changes, keeping local dev zero-dependency while staying production-ready. Enforced strict multi-tenant security - every route validates JWT ownership against the resource; PDFs compiled on-the-fly via PDFKit and streamed directly to the response socket, no files written to disk.
View ProjectDocket
June 18, 2026 – Present
Built a background worker that polls MongoDB on a cron loop, classifies note content via Gemini, and dispatches notifications fully decoupled from the request lifecycle with no user-facing latency impact. Implemented a dual-speed debounce queue: 10s window for time-sensitive inputs, 5-minute buffer for long-form entries - reduces redundant API calls while preserving responsiveness, rate-limited at 30 req/10s via Upstash Redis.
View ProjectInstaML
June 18, 2026 – Present
Architected three independent FastAPI services - Gateway for auth and orchestration, Trainer for Optuna hyperparameter search and K-Fold CV in background threads, Predictor for low-latency inference - each deployed separately on Render. Built a local-to-cloud file sync layer services write model artifacts to local disk and sync to Supabase; stateless containers pull missing files on demand, eliminating the need for a dedicated file server.
View ProjectLedger
June 18, 2026 – Present
Built a finance REST API with 3-tier RBAC, Zod validation on all inputs, and soft-delete for data integrity stack traces logged server-side only, sanitised error responses returned to clients.
View ProjectCultural Fit Analysis
The candidate's diverse personal projects, competitive hackathon achievements, and internships at Amazon and Expelee demonstrate a strong initiative and passion for AI and software development. Their academic background in AI and Data Science, coupled with practical application, aligns well with an AI Research Engineer role. The breadth of technologies used across projects indicates adaptability and a willingness to learn, which are positive indicators for cultural fit in a dynamic research environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project architectures (e.g., non-blocking job pipelines, local-to-cloud file sync, dual-speed debounce queues). Their experience at Expelee highlights an ability to own failure handling and iterate on operational data, indicating a proactive and resilient work attitude. The competitive selection for Amazon ML Summer School and hackathon achievements suggest a driven and collaborative spirit.