Software Engineer II @ American Express
Distributed Systems • Databases • Caching • AI Infrastructure
I'm a software engineer focused on the infrastructure behind modern software systems — distributed systems, databases, caching, and AI infrastructure.
I like understanding systems from first principles:
- How do systems behave under load?
- Where do they stop degrading gracefully?
- Why do failures propagate?
- How do databases and caches behave internally?
- What trade-offs exist between performance, correctness, and reliability?
I explore these questions through open-source contributions, reproducible experiments, systems projects, and technical writing.
I contribute to production infrastructure projects, primarily around correctness, crash-safety, reliability, and performance.
dragonflydb/dragonfly — high-performance in-memory datastore
My merged contributions include fixes across the core C++ engine involving:
- Cluster slot-migration correctness
- Production crash / SIGABRT root-cause analysis
- Cross-shard replication and geo-query crashes
- Sorted-set flag handling
- Search schema validation
- Scripting-path crashes
Several of these contributions are shipped in the v2.0.0 release.
valkey-io/valkey-search
Contributed fixes involving:
- Index-count correctness after coordinator RDB loads
- Thread-pool failure handling
- Shutdown and worker-state correctness
BetterDB-inc/monitor
Contributed reliability and security fixes around:
- MCP server HTTP error handling
- Internal error information exposure
- Docker networking / DNS behavior
I also have ongoing contributions across:
Valkey • FoundationDB • RocksDB • other distributed-systems projects
covering areas such as replication, persistence, indexing, pattern matching, and native memory management.
→ All Pull Requests
→ Dragonfly Commits
→ BetterDB Monitor Commits
Overload experimentation for distributed systems.
SlimyBug is a fault-injection and experimentation platform for studying how distributed systems behave under overload.
Instead of learning failure modes only from production incidents, SlimyBug creates controlled, reproducible experiments around:
- Retry amplification
- Cascading failures
- Circuit breakers
- Admission control
- Connection-pool saturation
- Overload onset
- Dependency latency propagation
- Retry jitter
- Admission deferral
- Connection-pool self-locking
Where exactly does a system stop degrading gracefully — and why?
I've run 12+ causal experiments measuring collapse boundaries and evaluating mitigation strategies. For example, a minimal circuit breaker reduced load reaching a saturated dependency by up to 44% in one experiment, followed by dedicated multi-run validation.
Stack: Python • FastAPI • PostgreSQL • Docker • Toxiproxy • Prometheus • Grafana • OpenTelemetry
Learned eviction policies for semantic LLM caches.
SmartEvict investigates whether lightweight learned policies can outperform traditional cache eviction heuristics for semantic LLM workloads.
The project benchmarks a Dueling DQN policy against:
- LRU
- FIFO
- GDSF
using real conversational workloads including LMSYS-Chat-1M and WildChat-1M.
📦 Repository
📖 Research Artifact / DOI
At American Express, I work on GenAI infrastructure and backend systems supporting internal financial-data workflows.
Some of the systems I've worked on include:
- Custom MCP infrastructure for contextual financial data access
- Graph-based financial data modeling using PostgreSQL + Apache AGE
- Two-layer semantic caching using pgvector + Redis
- LLM observability and tracing
- Agent memory infrastructure
- Sandboxed Python/Docker execution for data analysis
- FastAPI microservices and production backend infrastructure
Previously at EY, I worked on enterprise NLP/LLM systems, metadata intelligence, vector retrieval, and safety infrastructure for LLM-facing systems.
Across these systems, my focus has consistently been on performance, reliability, correctness, and productionization.
Distributed Systems
Databases & Storage Engines
Caching Systems & Eviction
Replication & Consensus
Performance Engineering
Reliability Engineering
AI / LLM Infrastructure
Developer Infrastructure
Real-Time Cryptocurrency Arbitrage Detection System
A real-time arbitrage detection system built around a high-performance C++ engine processing high-frequency price updates across exchanges.
Stack: C++ • WebSockets • CUDA • React
Dynamic inference optimization using Deep Q-Learning
Implemented early-exit strategies for CNN inference using Deep Q-Learning to dynamically determine when computation can stop while maintaining model accuracy.
Stack: Python • TensorFlow • Deep Q-Learning
I write about the experiments, investigations, and engineering lessons behind my systems work.
Topics include:
Distributed Systems • Databases • Caching • AI Infrastructure • Performance • Reliability • Software Engineering
Languages & Systems
Python • C++ • Go • Distributed Systems • System Design
Databases & Messaging
PostgreSQL • Redis • MongoDB • Kafka • RabbitMQ • Elasticsearch
AI / LLM Infrastructure
OpenAI API • LangChain • MCP • RAG • Vector Search • pgvector
Backend & Infrastructure
FastAPI • Flask • Microservices • Docker • AWS • GCP • CI/CD • Prometheus • Grafana • OpenTelemetry
- Cache internals & eviction algorithms
- Database internals
- Replication & consensus protocols
- Distributed-systems failure modes
- AI serving infrastructure
- LLM caching
- Performance engineering
- Reproducible systems experimentation
Build systems. Run experiments. Produce evidence. Share findings.
I believe the best way to understand complex systems is to build them, break them, measure them, and investigate why they behave the way they do.
🌐 Portfolio • 💼 LinkedIn • 📝 Medium • 📧 Email
Build systems. Run experiments. Produce evidence. Share findings.