I design and build production-grade backend systems that power enterprise healthcare platforms, AI applications, and distributed data pipelines at scale.
An AI assistant platform integrating OpenAI-compatible LLMs, LangChain, LangGraph, RAG, and Tavily Search.
Businesses needed AI assistants capable of retrieving internal knowledge and automating workflows while minimizing hallucinations and maintaining strict reliability.
Designed a modular RAG platform supporting semantic search, tool calling (MCP), agent orchestration, and conversation memory.
A configuration-driven enterprise platform enabling dynamic generation of operational pages and CRUD workflows without additional application development.
Enterprise customers required highly customizable workflows, but custom engineering for every client was unscalable. We needed a system supporting tenant isolation, multi-database switching, and dynamic schemas.
Architected a metadata-driven architecture that generates CRUD applications dynamically using configuration instead of hardcoded pages.
Backend services supporting enterprise Pharmacy Benefit Management (PBM) platforms handling complex healthcare and insurance workflows.
High-volume operational workflows required manual effort, and the existing infrastructure struggled with concurrent processing and caching inefficiencies.
Built scalable REST APIs and distributed job processing pipelines using Node.js, BullMQ, Redis, and MySQL.
A distributed data processing platform designed to ingest and process millions of records through configurable asynchronous pipelines.
Processing millions of enterprise records synchronously caused system bottlenecks, timeouts, and lacked fault tolerance and retry mechanisms.
Built a robust, configurable asynchronous pipeline with BullMQ workers, Redis, and MySQL supporting dynamic handlers and adjustable concurrency.
High-throughput data processing engine using background workers.
Node.js • BullMQ • Redis • MySQL
Handling rate limits, ensuring idempotency, preventing memory leaks during peak loads.
Traded immediate consistency for eventual consistency to achieve massive throughput and fault tolerance.
Deterministic multi-step agent orchestration with MCP.
LangGraph • OpenAI • Pinecone • MCP
Preventing infinite LLM loops and ensuring strict adherence to enterprise guardrails.
Sacrificed agent autonomy for predictable, graph-based deterministic execution paths.
Config-driven platform supporting strict data isolation.
TypeScript • Express • PostgreSQL • Redis
Managing dynamic schemas and preventing cross-tenant data leakage.
Used logical isolation (row-level) rather than physical databases to reduce infrastructure complexity.
Semantic search and retrieval augmented generation.
Python • LangChain • Pinecone • AWS
Optimizing chunk sizes for context windows while retaining semantic meaning.
Increased ingestion latency to generate high-quality dense vector embeddings for better retrieval.
Asynchronous task execution for high-volume workflows.
BullMQ • Redis • Node.js • Datadog
Handling stalled jobs and designing intelligent backoff strategies for third-party API failures.
Added Redis dependency specifically for robust job locking and delayed execution capabilities.
Centralized logging, metrics, and tracing.
Prometheus • Grafana • Winston • Elasticsearch
Structuring logs across distributed microservices without impacting application performance.
Sampled tracing at 10% in production to balance deep visibility against storage costs and overhead.
Great software is boring. It runs quietly, survives failures, scales predictably, and lets people focus on their work—not the infrastructure behind it.
Technology is never chosen in a vacuum. Every tool is a compromise.
Our use case required complex job state management — delayed jobs, rate limiting, and granular per-job retries with exponential backoff.
Chose BullMQ (backed by Redis) over Kafka's append-only log.
Out-of-the-box delayed jobs, retries, and rate limiting without the operational overhead of managing a Kafka cluster.
Healthcare platforms require strict data integrity, auditability, and complex relational policies (RBAC, tenant mappings).
Chose MySQL's ACID compliance over MongoDB's flexible schema design.
Reliable transactional guarantees, structured constraints, and predictable behavior for enterprise operational data.
In enterprise workflows, a hallucinated tool call or infinite reasoning loop is unacceptable.
Modeled AI workflows as explicit state machines using LangGraph.
Predictable execution paths with strict guardrails while still leveraging LLM reasoning for dynamic routing.
Enterprise API consumers (internal microservices, B2B integrations) required cacheable, standardized, and strictly versioned endpoints.
Chose REST over GraphQL for high-throughput predictable workloads.
Seamless HTTP-level caching (Redis/CDNs) and simpler observability overhead across the platform.
Our architecture is heavily I/O bound — DB queries, Redis caching, third-party API calls.
Chose Node.js's non-blocking I/O model over Go's raw concurrency.
Massive ecosystem for AI/LangChain integrations and rapid product iteration outweighed Go's raw performance for this phase of scale.
Hardcoding workflows for every new enterprise tenant was unscalable.
Abstracted business logic into a schema-less JSON configuration layer.
Dynamic generation of CRUD interfaces and tenant policies — shifted feature delivery from engineering to operations.
Building systems is only half the job. Keeping them reliable under real production load is where engineering is proven. The following incident is one representative example of how I approach diagnosis, mitigation, and long-term prevention.
A massive unexpected spike in healthcare data ingestion caused BullMQ to queue millions of jobs faster than the MySQL writer could consume them. Redis hit its max memory limit (OOM) and crashed, halting all asynchronous processing platform-wide.
Immediately scaled the Redis cluster, flushed failed jobs, and restarted worker pools to restore service. Then, implemented strictly bounded queues and aggressive BullMQ job TTLs to prevent queue bloat during upstream database degradation.
* Representative example from day-to-day production operations. Additional incidents omitted for brevity.
Architected and scaled multi-tenant platforms for high-volume healthcare workflows.