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Job Description :
Strong Expertise with Ruby:
Minimum 12 Years Of Experience On Ruby
Deep knowledge of Ruby MRI internals (GIL, memory model, object
allocation)
Ruby MRI garbage collector (mark & sweep, generational GC)
Strong experience with Rails internals (ActiveRecord, Arel, middleware
stack)
Ability to optimize slow queries, N 1 issues, and memory leaks
Experience with multi-threaded / concurrent Ruby (Puma, Sidekiq tuning)
Ruby VM instruction set (YARV bytecode basics)
Concurrent Ruby gem
Thread pools vs Fibers vs Ractors
Async frameworks (Falcon / Async gem)
Experience integrating Ruby with Kafka (ruby-kafka / Karafka), exactly-once
semantics handling
Designing: event schemas, schema evolution (Avro / JSON schema)
Proven experience scaling Rails apps to:
10K RPS
millions of daily transactions
Hands-on experience with:
Event-driven architecture (Kafka / RabbitMQ)
CQRS & Event Sourcing
Saga pattern (choreography & orchestration)
Strong understanding of:
Eventual consistency
Distributed transactions
Idempotent APIs
Deep understanding of:
Caching strategies (Redis, Memcached)
Background processing (Sidekiq, Resque)
Database sharding and read replicas
Strong experience with:
PostgreSQL (query tuning, indexing, vacuum, locking)
Redis (data structures, pub/sub, distributed locks)
Ability to debug:
deadlocks
slow queries
replication lag
Low-Latency / High-Throughput Optimization
Experience achieving
Queue prioritization strategies
Dead job recovery
idempotent job design
Redis contention debugging
Sidekiq middleware customization
Experience with:
AWS (EKS, RDS, SQS, Lambda)
Docker & Kubernetes
CI/CD pipelines (GitHub Actions / Jenkins)
Observability stack:
PCI-DSS exposure (preferred)
Secure API design (rate limiting, mTLS, encryption)
Experience working at:
high-frequency trading firms
payment processors
crypto exchanges
JRuby / TruffleRuby
GraphQL APIs
gRPC services
Exposure to:
fraud detection systems
real-time risk engines
Job Description :
Strong Expertise with Ruby:
Minimum 12 Years Of Experience On Ruby
Deep knowledge of Ruby MRI internals (GIL, memory model, object
allocation)
Ruby MRI garbage collector (mark & sweep, generational GC)
Strong experience with Rails internals (ActiveRecord, Arel, middleware
stack)
Ability to optimize slow queries, N 1 issues, and memory leaks
Experience with multi-threaded / concurrent Ruby (Puma, Sidekiq tuning)
Ruby VM instruction set (YARV bytecode basics)
Concurrent Ruby gem
Thread pools vs Fibers vs Ractors
Async frameworks (Falcon / Async gem)
Experience integrating Ruby with Kafka (ruby-kafka / Karafka), exactly-once
semantics handling
Designing: event schemas, schema evolution (Avro / JSON schema)
Proven experience scaling Rails apps to:
10K RPS
millions of daily transactions
Hands-on experience with:
Event-driven architecture (Kafka / RabbitMQ)
CQRS & Event Sourcing
Saga pattern (choreography & orchestration)
Strong understanding of:
Eventual consistency
Distributed transactions
Idempotent APIs
Deep understanding of:
Caching strategies (Redis, Memcached)
Background processing (Sidekiq, Resque)
Database sharding and read replicas
Strong experience with:
PostgreSQL (query tuning, indexing, vacuum, locking)
Redis (data structures, pub/sub, distributed locks)
Ability to debug:
deadlocks
slow queries
replication lag
Low-Latency / High-Throughput Optimization
Experience achieving
- sub-50ms API latency under load
- High throughput using connection pooling, query batching
- Puma workers vs threads trade-offs
- kernel tuning (ulimits, TCP tuning)
Queue prioritization strategies
Dead job recovery
idempotent job design
Redis contention debugging
Sidekiq middleware customization
Experience with:
AWS (EKS, RDS, SQS, Lambda)
Docker & Kubernetes
CI/CD pipelines (GitHub Actions / Jenkins)
Observability stack:
- Prometheus
- Grafana
- ELK / OpenSearch
PCI-DSS exposure (preferred)
Secure API design (rate limiting, mTLS, encryption)
Experience working at:
high-frequency trading firms
payment processors
crypto exchanges
JRuby / TruffleRuby
GraphQL APIs
gRPC services
Exposure to:
fraud detection systems
real-time risk engines