Shashank Yadav
Job Titles: Senior Software Engineer, Senior Backend Engineer, Backend Engineer, Ruby on Rails Developer, Rails Engineer, Platform Engineer, SDE II, Software Development Engineer, Full Stack Developer
Core Skills: Ruby, Ruby on Rails, Rails 4, Rails 5, Rails 6, Rails 7, Sidekiq, Sidekiq Pro, Redis, MySQL, SQL, read replicas, tiered caching, background jobs, async processing, job queues, batch processing, bulk inserts, concurrency tuning, queue topology, idempotency, checkpoint orchestration, pause resume jobs, failover, disaster recovery, capacity planning, query optimization, schema optimization, N+1 queries, API throughput, REST API, multi-tenant, Docker, containerized services, Databricks, data warehouse, MongoDB, 100 million records, 200 million jobs per day, infrastructure cost reduction, pod optimization, Kubernetes, distributed systems, microservices, monolith modernization, Rails engines, Ruby gems, TDD, test driven development, Agile, Scrum
AI Skills: Cursor Pro, AI-assisted development, agentic AI, AI agents, MCP Model Context Protocol, Jira integration, Airbrake integration, automated triage, pull request automation, code review automation, productivity engineering, human in the loop, secure AI adoption
Companies: Punchh, PAR Technology, Daffodil Software, CampusBox
Education: B.Tech Bachelor of Technology, Galgotias College of Engineering and Technology, GATE 2018 Qualified
Location: Gurugram, Gurgaon, Haryana, India, NCR
Contact: shashank0x1@gmail.com linkedin.com/in/iamshashankio github.com/iamshashank
Impact Metrics: 35 percent pod reduction, 35 percent infrastructure cost savings, 200M+ background jobs daily, 100M+ row datasets, checkpoint resume, tiered caching, read replica routing
Recognition: Hero Award Punchh PAR 2020 2021
Professional Summary
Senior Software Engineer with 8+ years building and operating high-throughput
Ruby on Rails backends in production. Deep experience in async job systems
(Sidekiq), read-replica and tiered caching strategies, and reliability
patterns—failover, checkpointing and idempotency for systems processing 200M+ background jobs daily. Delivered measurable
business impact including ~35% reduction in worker pod count via batching inserts and better pod utilization,
improved stability and reliability of background jobs, and modernization of internal systems for scale and cost efficiency.
Actively applies agentic AI workflows to accelerate triage, debugging, and delivery. GATE qualified (2018).
Technical Skills
- Languages
- Ruby, SQL, JavaScript (prior full-stack)
- Frameworks
- Ruby on Rails 4.x–7.x, Rails engines & internal gems
- Async & cache
- Sidekiq (Pro patterns), Redis, tiered caching, job deduplication & locking
- Data & scale
- MySQL (query/schema optimization), read replicas, bulk pipelines, 100M+ row processing
- Reliability
- Checkpoint orchestration, failover design, idempotent workers, capacity planning
- Platform
- Multi-tenant services, Docker, API throughput tuning, TDD, Agile
- AI & tooling
- Cursor Pro, MCP agent orchestration, Jira & Airbrake integrations, AI-assisted triage & code review
AI-Assisted Engineering
Hands-on practitioner of AI-augmented development—building practical agent workflows that compound engineering output
while respecting enterprise security boundaries.
- Built custom AI agents on Cursor Pro with MCP integrations to Jira and Airbrake that monitor assigned tickets, correlate production errors with local codebase context, and surface likely root causes before deep manual investigation.
- Designed an end-to-end workflow where agents analyze code on the local machine and produce suggested PR fixes with detailed comments and rationale—compressing time from ticket assignment to review-ready change sets.
- Enforced strict data-access guardrails per company policy: production database access is not granted to local AI agents, demonstrating security-conscious adoption that prioritizes productivity without exposing sensitive systems.
- Continuously refines agent prompts, tool boundaries, and human-in-the-loop review to improve signal quality, reduce toil, and increase ship velocity across backend and async job systems.
Key Impact
| Sidekiq / workers | Higher throughput; ~35% fewer pods for comparable load |
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| Data scale | Background jobs to run with over 100M+ records with checkpoint resume |
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| Platform volume | Systems operating at 200M+ jobs/day throughput |
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| Cost efficiency | Direct infra savings via pod count reduction, workflow and code optimization |
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| AI productivity | Custom Jira/Airbrake agents for faster triage and PR-ready fixes from assigned tickets |
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Professional Experience
Senior Software Engineer (Mar 2024 – Present) · SDE II (Nov 2021 – Present)
- Scaled async processing for a platform running 200M+ background jobs/day by redesigning Sidekiq workflows—queue topology, batching, concurrency tuning, and hot-path refactors.
- Reduced infrastructure cost ~35% by
optimizing worker efficiency and right-sizing pod counts while
maintaining SLAs through profiling and adding safe pause/resume
checkpoint to long running background jobs.
- Introduced tiered caching and read-replica routing to offload primary database pressure and improve API and job throughput under peak campaign load.
- Revamped internal systems to use Databricks so single background job can safely work with 100M+ row datasets with checkpoint-based orchestration so long-running bulk jobs resume safely after failure without full restarts.
- Designed failover for scenarios where sidekiq redis memory reached critial levels for critical pipelines
- Extracting out re-usable logic for ( failover, checkpoints ) into reuasble component cross services
Stack: Ruby on Rails, Sidekiq, Redis,
MySQL (+ replicas), Wharehouse (Databricks), Mongo DB, POS, multi-tenant
configuration, containerized services
Associate IT — Full Stack Ruby on Rails
- Upgraded Rails monoliths from 4 → 5 → 6; improved
stability and long-term maintainability across e-commerce and
real-estate products.
- Reduced page load times by fixing N+1 queries, schema refactors, Redis caching, and strategic memoization.
- Modularized monoliths by extracting reusable domains into Rails engines packaged as internal gems for cross-project reuse.
AngularJS Frontend Developer (Intern)
Education
B.Tech — Galgotias College of Engineering & Technology
2014 – 2018
GATE 2018 — Qualified
Recognition
Hero Award — Punchh / PAR (2020–2021)