Vetted Batch Processing Professionals

Dhairya Gajjar

Screened

Mid-Level Software Engineer specializing in Healthcare Data Platforms

Remote2y exp

“Backend/ML engineer with healthcare domain experience building secure Medicare/Medicaid data APIs and real-time patient risk scoring. Shipped an end-to-end ML pipeline (scikit-learn/XGBoost) served via SageMaker and integrated into Flask APIs, with strong production reliability practices (Kafka schema validation, regression replay, observability, drift monitoring, and human-in-the-loop guardrails).”

Vinith Nagelly

Screened

Mid-level Full-Stack Developer specializing in Python/Django and React

Little Rock, AR4y exp

“Backend engineer at Hexanika who owned a real-time fraud-detection platform: built Django microservices, integrated a GenAI anomaly-scoring model, and optimized data/infra for low-latency production (including ~40% query-latency reduction). Experienced running containerized services on AWS/GCP with Kubernetes/GKE, GitHub Actions-based CI/CD + GitOps, and building Pub/Sub streaming pipelines and on-prem-to-cloud migrations.”

Divya Patel

Screened

Mid-Level Software Engineer specializing in .NET and CMS platforms

Fremont, CA6y exp

“Built and owned end-to-end systems for the Department of Water Resources and NAHC, including a debt infrastructure management system and a TypeScript/React + .NET 6 CMS. Strong in shipping quickly with quality (CI/CD, automated testing), optimizing SQL Server performance for large datasets, and implementing microservices-style async processing with reliability patterns (retries/idempotency/monitoring). Also delivered widely adopted internal workflow automation and reporting using Power Automate and Power BI.”

“Built and shipped a production LLM-powered document processing agent (ingestion→OCR→LLM extraction/classification→validation→DB/workflow triggers) using Kafka and PostgreSQL. Emphasizes production reliability with strict JSON schemas, idempotent services, retries/backoff, fallback models, and human-in-the-loop review—driving ~90% automation, minutes-to-seconds processing, and ~10x throughput.”

“Backend/data engineer with hands-on production experience building a FastAPI Python service on AWS for real-time AI workflows (Postgres/Redis, containers behind API Gateway) with strong reliability practices (JWT auth, timeouts/retries, health checks). Has delivered AWS infrastructure using Terraform + GitHub Actions across environments, built Glue ETL pipelines into Snowflake with idempotent recovery, and modernized legacy batch workflows via parallel-run parity validation and phased cutovers.”