Abhigyan Agarwal

Software Engineer

India5 yrs 2 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in architecting robust solutions at scale
Recognized for resolving critical production issues
Passionate about AI/ML Infra and foundational systems

Stackforce AI infers this person is a Backend-heavy Infrastructure Engineer with expertise in large-scale systems.

Contact

abhigyanagarwala@gmail.com LinkedIn

Skills

Core Skills

Distributed SystemsSoftware Development

Other Skills

Adobe IllustratorAdobe PhotoshopAlgorithmsC (Programming Language)C++Code DesignCode ReviewData Structures and AlgorithmsJavaKeyboardistMicroservicesMySQLNetwork InfrastructureNetwork telemetryProblem Solving

About

I am a problem solver at heart, currently working as a Software Engineer in Google's Technical Infrastructure. With a foundation in competitive programming and a passion for foundational, systemic challenges, I enjoy architecting robust solutions for complex problems at massive scale. At Google, I've spent three years tackling challenges within the large-scale network telemetry space. This experience has given me a deep, hands-on understanding of what it takes to build and maintain reliable, high-performance systems that operate at planet scale. What truly drives me is the satisfying struggle of a difficult problem. My approach is rooted in first principles thinking, thoroughly understanding the fundamental truths of a system. This has enabled me to resolve some of our team's most challenging system-wide production issues, an effort that has been recognized with multiple Spot and Peer awards. While I enjoy the reactive thrill of debugging, my passion lies in applying that knowledge proactively to the design and foundational stages. I enjoy anticipating corner cases and architecting resilient systems from the ground up to prevent problems before they happen. Currently, my interests are centered on AI/ML Infra, R&D, and foundational systems engineering. I'm actively deepening my knowledge in these areas through self-study and personal research, and am excited to build the next generation of innovative, high-impact products. I'm always open to connecting with people who are passionate about building the future of technology.

Experience

Google

Software Engineer

Jul 2022 – Present · 3 yrs 8 mos · Bengaluru, Karnataka, India · On-site

Worked on core feature development, reliability, and scaling for Google's large-scale network telemetry systems (NetSLO, gTIB) that maintain Google's production network health across XX million machines, processing XX billion probes per second for real-time alerting, SLO reporting and automated issue diagnosis.
Key Contributions:
● Resolved Systemic Data Consistency Issues & Optimized Performance
› Conducted a full-stack root cause analysis, diagnosing multiple interacting failure modes (client-side cache invalidation, server restarts, DB eventual consistency).
› Designed and implemented a multi-layered solution that addressed the root causes by implementing a 'single source of truth' (SOT) abstraction layer, fixing client-side caching, and adding server-side cache pre-warming.
› Impact: Eliminated a class of bugs (200+ prod issues), yielding major gains in stability & performance:
✓ Reduced p99 latency by >75% (>2 min to <30s).
✓ Decreased p90 latency by >83%.
● Production Reliability & Incident Response
› Enhanced service reliability by leading large-scale incident response, authoring post-mortems & resolving critical data accuracy bugs.
› Improved stability by building automated monitoring to detect & alert on pipeline bottlenecks before service impact.
● Optimized High-Throughput Data Pipeline
› Engineered a solution to efficiently build large protobuf messages in a high-throughput pipeline, ensuring strict size compliance without performance degradation.
● Accelerated Development Velocity
› Designed & implemented an integration test framework, enabling robust E2E validation and empowering the team to ship features faster & with higher confidence.
● Expanded Network Monitoring Coverage
› Drove the expansion to critical Edge networks by proposing a major redesign of the resource scheduling.
› Additionally, extended monitoring of the Control Plane network increasing overall network monitoring coverage.