Software Development Engineer with 3 years of experience at Amazon building large-scale AWS infrastructure for a SaaS seller payments platform. Proficient in Java, microservices, and cloud-native development using EC2, ECS Fargate, Lambda, DynamoDB, ElastiCache, API Gateway, CloudFormation, and CloudWatch.
Led end-to-end delivery of multiple projects at Amazon — including a regulatory data localization solution and a cross-team microservice integration — independently owning high-level design, low-level design, implementation, and integration testing.
Beyond cloud engineering, I have completed a structured, hands-on deep-dive into agentic AI engineering — building and deploying multi-agent systems using OpenAI Agents SDK and CrewAI. Projects include a live AI career assistant on HuggingFace Spaces, a deep research agent pipeline, a sales automation crew, and a stock analysis system. I work at all abstraction levels: raw LLM API, SDK, and framework.
Pursuing AWS Certified Solutions Architect – Associate certification. Seeking an SDE or AI Engineer role in a product-focused team where I can contribute from day one.
Worked on a large-scale SaaS seller payments platform built entirely on AWS using a microservices architecture.
Led end-to-end design and implementation of a data localization solution to meet Indian data residency regulations. Requirement: all India-seller data (seller ID, marketplace details, card information) must be stored within India and must not reside outside for more than 24 hours. Created a dedicated DynamoDB table, an ElastiCache caching layer, and architecture-level routing changes. Independently owned HLD (approved by senior engineers), LLD, implementation, and end-to-end testing.
Designed and implemented a complete integration between the team's microservice and an upstream microservice responsible for deferring transactions. Built a custom graph data structure where each node stores a JSON value from the upstream service's output payload. Owned full lifecycle — HLD, LLD, implementation, and integration test suite.
Built an internal AWS Lambda tool that took JSON event data as input and generated the corresponding CFE (Canonical Financial Event) document — a graph-structured data model with entities, fields, and entity relationships. Built during the team's re-architecture from JSON event storage to the CFE document format; used to test data-model definitions against real JSON inputs and debug the generated CFE documents, accelerating the team's modeling workflow.
Building production-grade serverless AWS applications and agentic AI systems.
I take projects from the design table to production: high-level design (with senior approval), low-level design, implementation, integration tests, and end-to-end testing. I owned three Amazon projects this way — including a regulatory-compliance data localization rollout that required architecture-level routing changes across the service.
Shipping is the start, not the end. I built CloudWatch dashboards and configured alarms on the seller payments platform to catch production issues early and reduce MTTR. Comfortable triaging incidents, reading logs at depth, and writing root-cause analyses.
I held an active review presence on the team — reviewing PRs, pushing back on design shortcuts, and treating integration test coverage as part of "done," not an afterthought.
Mentored two interns at Amazon — onboarded them onto the codebase, paired on their first features, and reviewed their designs and code. Both shipped meaningful work by the end of their internships.
My biggest Amazon projects required negotiating integration contracts with upstream teams. I keep written, traceable communication and surface trade-offs early — not at integration time.
When my team was repeatedly decoding Base64 event payloads by hand for debugging, I shipped a small AWS Lambda tool that did it in one click. Small problems, well-fixed, compound across a team.
Fully serverless web application enabling businesses to collect customer testimonial videos via a shareable link. Videos stored in S3 with instant SNS notification on upload. Business sends link → customer records → video stored → notification sent.
End-to-end deployment automation from GitHub push to production with zero-downtime releases. CloudFront edge caching integrated for global latency reduction across all regions.
Event-driven system for automated data ingestion and real-time alerting. Pattern matching filters high-volume event streams before triggering SNS notifications. Deployed with AWS SAM.
Regulatory compliance solution at Amazon ensuring India-seller data is stored within India within 24 hours. Dedicated DynamoDB table, ElastiCache caching layer, and routing architecture changes.
Built during a structured AI agentic engineering course (Ed Donner) — all projects deployed, not just studied.
A Gradio chatbot deployed on HuggingFace Spaces that reads my LinkedIn PDF, answers questions as me, and when a recruiter is interested, captures their contact info via tool calls and sends a push notification.
Fully automated research pipeline: given a query, a Planner Agent designs searches, Search Agents execute them in parallel, a Writer Agent synthesizes a structured report, and an Email Agent delivers it — with live streaming UI.
Three specialist writing agents (Professional, Engaging, Concise) each generate a sales pitch. A Manager Agent selects the best one and downstream agents format and send the outreach email automatically.
End-to-end investment research pipeline: finds trending companies via web search, performs deep financial research on each, and produces a structured investment recommendation with push notification delivery.
Three-agent CrewAI crew that autonomously generates a complete website: HTML/CSS/JS frontend with image slider, AWS Lambda Python backend, and a GitHub Actions CI/CD deployment workflow using OIDC.
Two crews: a Financial Researcher (researcher + analyst agents) producing full markdown reports on any company, and a Debate Crew (topic selector, pro, con, judge) producing written arguments and a final verdict.
Received pre-placement offer from Amazon through on-campus recruitment in 3rd year.
I'm currently open to Software Development Engineer roles. Feel free to reach out directly.
