Abbas Khan

Full Stack Engineer

Hyderabad, Telangana, India2 yrs 11 mos experience
Most Likely To SwitchAI ML Practitioner

Key Highlights

  • Proven track record of delivering measurable business impact.
  • Strong end-to-end ownership in software development.
  • Expertise in building scalable distributed systems.
Stackforce AI infers this person is a Backend-heavy Fullstack Engineer with expertise in E-commerce and Cloud-based solutions.

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Skills

Core Skills

JavaMicroservicesAwsPython

Other Skills

JavaScriptKafkaSpringCloudFormationECSLambdaDynamoDBKinesisOpenSearchREST APIsElasticsearchOOPSAlgorithmsAmazon KinesisAmazon SQS

About

Software Development Engineer @ Amazon with 2.5+ years of experience building and operating large-scale distributed systems across Worldwide Returns & Recommerce (WWRR), Expansions Tech & Product, and Pricing platforms, powering customer-facing return, refund, and pricing experiences across global marketplaces. I’m a backend-heavy Java engineer with strong end-to-end ownership — from HLD/LLD and API design through implementation, deployment, and on-call operations. I’ve owned and delivered core Customer Return Experience (CREX) features (Return Items, Post-Return Experience) and designed reusable, widgetised architectures to improve consistency, reuse, and developer velocity at scale. In WWRR, I helped redesign the Online Return Center (ORC) customer pages and backend contracts, enabling cleaner service boundaries and domain models. This reduced coupling and cut change-related defects by ~25% while supporting millions of annual return events. I built two internal, customer-facing APIs following AWS engineering and security standards, and led integrations with external clients to automate migration from deprecated packages to newer platforms, reducing manual effort and improving maintainability. I drove enhancements to returns and refunds orchestration, improving return completion rates and reducing downstream reprocessing and customer re-contact. This included strengthening refund notifications, adding refund SLA validation, and integrating a new payment instrument , supporting millions of dollars in annual refund volume. I also contributed to MTRI return flows, integrating backend orchestration with agentic LLM workflows using Amazon Bedrock, reducing incorrect refunds and manual reviews and contributing to ~$5–10MM in annualized savings. On the Pricing platform, I designed pricing entities and filtering logic for offer disqualification (FOD) and merchant selection. Across Expansions and Pricing, I built event-driven, resilient services for new marketplace launches, achieving ~20–30% reduction in failure retries, improved P99 latencies, and lower on-call load. I have a proven track record of delivering measurable business impact, obsess over customer success, enjoy solving complex technical challenges, and am passionate about mentoring peers. Tech: Java, Spring, Microservices, Kafka, AWS (ECS, Lambda, DynamoDB, SNS/SQS, S3, OpenSearch, CloudWatch,Scala, Typescript, JavaScript.

Experience

2 yrs 11 mos
Total Experience
1 yr 5 mos
Average Tenure
2 yrs 6 mos
Current Experience

Amazon

Software Development Engineer

Dec 2023Present · 2 yrs 6 mos · Hyderabad · On-site

  • Working as an SDE at Amazon across Expansions Tech & Product, Worldwide Customer Returns & Recommerce (WCRR), and Pricing platforms. I engineered and optimized Online Return Center (ORC) workflows to simplify the returns journey, integrated new payment methods and delivery carriers, and designed scalable, configuration-driven architectures to accelerate global marketplace onboarding. I delivered Prime and Peak Readiness initiatives, improved system reliability through expanded Unit, Integration, and E2E test coverage, and strengthened CI/CD pipelines to support multi-region deployments across EU, NA, and FE.
  • I owned on-call operations, resolved high-severity production incidents, and led JDK and AWS CDK migrations. I designed secure REST APIs (Smithy, Swagger) with strong PII protection, built CloudWatch dashboards and alarms, and managed AWS infrastructure using ECS, Lambda, Route53, SQS/SNS, DynamoDB, CloudFormation, and CDK (TypeScript), with core backend development in Java and Guice. I improved system scalability by ~50% using an event-driven architecture with Apache Kafka (MSK), and built an internal ticketing and debugging chatbot using Amazon Bedrock (Claude multimodal) with asynchronous workflows.
  • In parallel, I worked on Pricing and Offer Disqualification systems, implementing merchant eligibility filtering using Kinesis, Lambda, and DynamoDB. I designed resilient pricing ingestion pipelines with SQS DLQs for failure tracing, enabled low-latency analytics via OpenSearch, modernized ~20,000 CI/CD pipelines using Docker, AWS Fargate, ECS, and EventBridge, and contributed to frontend development using React.js for internal UI widgets and pub/sub–based event triggering.Built DynamoDB-backed caching with optimized TTL achieving ~90% cache hit ratio, significantly reducing backend reads and improving response latency. Developed rate limiting and throttling mechanisms to protect downstream services and ensure SLA compliance.
JavaJavaScriptAWSMicroservicesKafkaSpring+4

Ct university aws academy

AWS Cloud Computing Intern

Mar 2023Aug 2023 · 5 mos

  • Owned end-to-end AWS cloud integration, improving system stability by 25–30% through VPC networking, Python automation, and distributed service debugging. Built scalable real-time and batch pipelines using AWS Glue, MSK (Kafka), Kinesis, Spark, and Redshift, processing 5–10M events/day with 99.9% reliability. Developed high-performance REST APIs for OpenSearch, reducing latency by 30–40% and doubling throughput through JVM and query optimization.Implemented secure authentication using OAuth 2.0, Active Directory, Okta, and Cognito across Neptune, Redshift, and OpenSearch with zero security incidents. Led Elasticsearch to OpenSearch migration, improving availability to 99.95% and reducing operational overhead by 30%. Automated infrastructure provisioning with CloudFormation, cutting setup time by 60–70%.Optimized Neptune Gremlin queries and Redshift workloads using cache tuning, sort keys, vacuuming, and distribution styles, improving query performance by 25–45%. Automated ETL workflows, managed OpenSearch index lifecycles, and integrated Python utilities into microservices, improving pipeline efficiency by 40–50%.Built a trie-based search engine reducing substring search time by 80% and memory usage by 60% compared to Elasticsearch. Developed a secure SSO customer portal and GraphQL gateway for REST services, reducing latency by 40%. Implemented Java microservices and Redis caching, improving scalability by 50% and performance by 20%.Delivered 6x PLM platform performance improvement using Neptune Graph DB for BOM dependency queries with sub-second response times.
AWSPythonREST APIsOpenSearchElasticsearchCloudFormation

National institute of technology, tiruchirappalli

Research And Development Intern

May 2022Jul 2022 · 2 mos

  • Optimization analysis using Greedy algorithms
  • Tech stack used:Java, OOPS, Algorithms, Fullstack development.
JavaOOPSAlgorithms

Aptransco

Project Engineering Intern

May 2021Jul 2021 · 2 mos

  • Completed industrial training at APTRANSCO 400/220 kV EHV Substation, gaining exposure to power transmission systems, substation operations, and maintenance. Studied 400/220 kV transformers, OLTC, ONAN/ONAF cooling, CTs, PTs/CVTs, SF₆ circuit breakers, isolators, bus bars, lightning arresters, and protection systems. Developed knowledge of SLDs, SCADA, numerical relays, switching operations, testing procedures, and EHV safety practices.

Education

Jawaharlal Nehru Technological University Anantapur (JNTUA)

Bachelor of Technology - BTech — Electrical and Electronics Engineering

Jan 2019Jan 2023

Hyderabad Institute of Excellence - India

Intermediate(MPC)

Government High School, Pileru

SSC

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