Vetted Amazon EMR Professionals

“Built a production-grade LLM evaluation and regression system that stress-tests models across hundreds of iterations, combining LLM-as-judge, semantic similarity, statistical metrics, and rule-based checks, with results delivered via stakeholder-friendly HTML reports and dashboards. Experienced orchestrating multi-agent RAG workflows using LangChain/LangGraph and event-driven GenAI pipelines in n8n integrating OCR, speech-to-text, and external APIs, with strong emphasis on reliability, observability, and explainable failures.”

“Data/ML platform engineer with ~6 years in financial services and enterprise data platforms, building regulated fraud/credit-risk pipelines on AWS (Airflow, EMR/Spark, MLflow) and an Azure lakehouse ingesting 50+ sources and serving ~100M records/day. Also led an early-stage deployment of a RAG-based internal AI search tool using AWS Bedrock and LangChain with automated evaluation to validate LLM accuracy.”

Vengalarao Pachava

Screened

Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems

Irving, TX2y exp

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

Himanshu Shivkumar Garg

Screened

Senior Full-Stack Java Developer specializing in Healthcare IT and FinTech

Florida, USA4y exp

“Built and owned an end-to-end HIPAA-compliant Next.js/TypeScript portal at Casedok using a microservices, event-driven architecture with Temporal-orchestrated SAGA workflows. Emphasizes production-grade quality (strict typing, Jest integration tests, CI + mandatory PR reviews) and operational reliability/observability (circuit breakers, idempotency, Prometheus alerts), plus experience designing external-facing APIs with Swagger, JWT, and backward-compatible versioning.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Software engineer with hands-on experience spanning backend APIs, streaming data systems, and cloud/infrastructure automation, who is already using agentic AI workflows in a disciplined way. Stands out for combining practical systems work in Spring Boot, Kafka/Spark/ClickHouse, and Terraform/Kubernetes with a thoughtful approach to AI oversight, architecture, and multi-agent orchestration.”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Backend-leaning full-stack developer (Java/Spring Boot, Angular, MySQL) who emphasizes rapid iteration with quality via clean code, validation, and API testing. Built a web application to help women connect directly with NGOs, focusing on an intuitive dashboard and user walkthroughs to support adoption, and designed flexible REST APIs to handle frequent requirement changes.”