Personal Information
Target Role
Professional Summary
Education
Technical Skills
Soft Skills
Work Experience
Certifications
Projects
Personal Details
ARJUN MEHTA
M.S. Computer Science | Stanford University
Professional Summary
Senior Software Engineer with 6 years of experience at product companies. Built systems serving 50M+ users. Expert in distributed systems, system design, and leading cross-functional teams. Looking for Staff/Principal Engineer opportunities.
Education
| Degree & Year | Institute | Board | Grade |
|---|---|---|---|
| M.S. Computer Science (2016–2018) | Stanford University | GPA: 3.92/4.0 — Turing Fellowship | 3.92/4.0 |
| B.Tech CSE (2012–2016) | IIT Bombay | Institute Rank 7 | 9.4/10 |
Technical Skills
- Java / Kotlin / Go
- System Design / Distributed Systems
- Apache Kafka / Flink
- MySQL / Cassandra / DynamoDB
- React / TypeScript
- AWS / GCP (Expert)
- Kubernetes / Terraform
- Machine Learning / TensorFlow Lite
- gRPC / Protobuf
- A/B Testing / Experimentation
Soft Skills
Certifications
- Google Cloud Professional Architect
- AWS Solutions Architect Professional
- Certified Kubernetes Administrator (CKA)
Work Experience
Senior Software Engineer — Ads Infrastructure — Google
Jan 2020 – Present
- Led design and launch of real-time bidding engine processing 500K QPS, generating $120M in additional annual revenue
- Reduced ads serving latency by 42% (P99) by redesigning cache invalidation architecture across 6 data centers
- Mentored 4 junior engineers to promotions; chaired 12 system design interviews quarterly
Software Engineer II — Flipkart
Jul 2018 – Dec 2019
- Architected product search ranking system serving 10M+ queries/day during Big Billion Days
- Improved click-through rate by 18% using collaborative filtering recommendations
- Built A/B testing framework used by 25+ teams to run 200+ experiments simultaneously
Projects
Distributed Cache System | Java, Consistent Hashing, Redis, gRPC
• Designed a distributed cache handling 1M+ ops/sec with automatic sharding and replication • Achieved 99.999% availability using leader election and consensus via Raft protocol • Reduced cache miss rate by 35% using predictive pre-warming based on traffic patterns
ML Feature Store | Python, Apache Feast, Spark, BigQuery
• Built an ML feature store serving 500+ features to 30 production ML models • Cut feature computation cost by 70% through cross-model feature sharing and materialization • Reduced model training time from 4 hours to 22 minutes using optimized batch pipelines
Personal Details
| dob | 15/08/2002 |
| gender | Male |
| father Name | Robert Doe |
| address | 123 Tech Street, San Francisco, CA 94102 |
| phone | +1 (555) 123-4567 |
| languages | English, Spanish |
| interests | Open Source, Competitive Programming, Hiking |
Place: San Francisco
Date:
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