I design and implement scalable microservice architectures that support real-time analytics, ML inference, and data-driven application features. Services are containerized and exposed through well-defined API layers, enabling independent deployment, horizontal scaling, and clear service boundaries. I use event-driven patterns to orchestrate asynchronous workflows, trigger downstream processing, and decouple ingestion from inference and persistence layers. Architectures are built to support heterogeneous client data, enforce access isolation, and maintain predictable performance under production load. Where appropriate, I combine serverless and container-based infrastructure to balance cost efficiency with latency and throughput requirements. Systems are instrumented with structured logging and monitoring to ensure traceability and rapid fault isolation in live environments.
Representative Work
- Designed and deployed a scalable AWS-based microservice architecture supporting real-time analytics and ML inference across client-specific schemas.
- Implemented event-driven ingestion workflows using managed cloud services to normalize, validate, and route data into downstream inference and storage layers.
- Built containerized backend services exposing ML-driven features through API gateways, enabling controlled rollout and independent service scaling.
Core Technologies
Containerized microservices; API-based service design; event-driven orchestration; serverless compute; container services; asynchronous processing patterns; structured logging and monitoring; hybrid serverless/container deployment models; horizontal scaling strategies.
I design and operate data platforms that support both analytical workloads and production ML inference across evolving schemas. My work includes building ingestion layers that normalize heterogeneous client data into structured, queryable formats suitable for downstream modeling, reporting, and feature generation. I implement batch and streaming pipelines that balance throughput, latency, and cost constraints while maintaining data integrity guarantees. Architectures are structured to separate raw storage, processed data layers, and feature-ready outputs, enabling reproducibility and controlled reprocessing. For high-volume transformations and embedding workflows, I leverage distributed processing frameworks to ensure scalability without compromising traceability. Systems are built with validation checkpoints and monitoring hooks to maintain data quality across the lifecycle.
Representative Work
- Built a large-scale data platform capable of ingesting structured and unstructured datasets, performing feature engineering, and serving derived insights into application backends.
- Designed event-driven ETL workflows to normalize client-specific schemas, enabling real-time and batch inference across heterogeneous enterprise datasets.
- Implemented distributed processing pipelines supporting large-scale NLP and embedding workloads for both offline training and production feature generation.
Core Technologies
ETL and ELT pipeline design; schema normalization and transformation layers; data lake architectures; distributed processing frameworks; batch and streaming workflows; embedding generation pipelines; feature engineering infrastructure; relational and analytical data stores; data validation and quality checks; reprocessing and backfill strategies; scalable storage and compute separation.
I design infrastructure that supports repeatable, testable, and observable deployment of backend and ML systems. Environments are defined through infrastructure-as-code to ensure reproducibility across development, staging, and production. I implement CI/CD pipelines that automate build, test, containerization, and deployment processes, reducing risk during feature releases and model updates. Systems are instrumented with monitoring, logging, and alerting to provide visibility into service health, data integrity, and inference performance. I incorporate rollback strategies, controlled rollout patterns, and explicit failure handling to mitigate operational risk. My focus is long-term maintainability — ensuring systems remain stable as data volumes, client configurations, and feature complexity grow.
Representative Work
- Established infrastructure-as-code patterns and CI/CD workflows to standardize deployment across backend and ML services within an enterprise analytics platform.
- Implemented automated database migrations, validation checks, and deployment safeguards to reduce production regressions during feature expansion.
- Designed monitoring and observability patterns enabling rapid identification and remediation of inference failures, schema inconsistencies, and performance bottlenecks.
Core Technologies
Infrastructure-as-code frameworks; CI/CD pipeline automation; container build and deployment workflows; automated testing and validation layers; structured logging; monitoring and alerting systems; deployment rollback strategies; controlled release patterns; environment configuration management; cost and performance monitoring; incident response workflows.