Vetted Stored Procedures Professionals

“Data engineer with a Master's in Data Science who has owned 30+ customer-facing K-12 SIS migrations end-to-end, building ETL, validation, and SOP-driven deployment processes in a PII-sensitive environment. Also brings recent hands-on agentic AI experience from a biotech capstone, where they led a production-oriented NLP-to-SQL + RAG support system that handled about 30% of support queries in testing.”

“Built and shipped production LLM/RAG systems at Mayo Clinic, including a conversational AI assistant for patient pre-consultation and a clinical-trial matching tool for doctors. Implemented HIPAA-compliant de-identification and guardrails, plus real-time feedback logging and fine-tuning that improved response accuracy by 15% and reduced admin workload by 25%.”

“Data engineer at Capital One building AWS-based real-time and batch pipelines and backend data services for financial/fraud use cases. Has owned end-to-end pipelines processing millions of records/day, implemented dbt/Great Expectations quality gates, and tuned Redshift/Snowflake workloads (cutting query latency ~22–25% and reducing pipeline failures ~30–40%) while supporting 15+ downstream consumers.”

Eduardo Diaz

Screened

Senior Full-Stack Software Engineer specializing in Python, FastAPI/Django, and Azure

Remote13y exp

“Backend/data engineer with production experience building real-time IoT telemetry pipelines for wind/solar assets at Siemens (FastAPI on Azure Event Hubs/Service Bus, Cosmos DB + SQL Server) and deploying GPS/fleet telematics microservices on AWS ECS Fargate with Terraform and blue/green CI/CD. Demonstrated strong reliability and performance chops, including a 30s-to-<100ms SQL optimization and owning a Kafka pipeline incident resolved in ~20 minutes.”

Sai Naveen Masadi

Screened

Senior .NET Full-Stack Developer specializing in cloud-native enterprise apps

Irving, TX5y exp

“Full-stack TypeScript engineer who built and operated a production order/inventory platform (React + NestJS/Node + PostgreSQL) with Redis and RabbitMQ for performance and background workflows. Emphasizes correctness in production via idempotency, retries/backoff, DLQs, and observability, and has also delivered external-facing REST APIs (Swagger, versioning, JWT/RBAC) plus resilient checkout browser automations using Playwright/Puppeteer.”

Mathew Martin

Screened

Mid-level Software Engineer specializing in cloud data platforms and LLM applications

New York, USA4y exp

“LLM/agent builder with experience shipping production LLM features at an early-stage ed-tech mental wellness startup (conversation analysis + structured feedback via FastAPI, OpenAI API, Render, CI/CD). Also built a multi-step dining concierge agent using OpenSearch over Yelp data with fallback query relaxation, and has enterprise data engineering experience at Capgemini migrating databases to Snowflake with robust ETL normalization and data-quality handling.”

Sushma Mangalampati

Screened

Mid-level Data Engineer specializing in lakehouse ETL and analytics engineering

Boston, MA6y exp

“Data engineer with strong end-to-end ownership of production lakehouse pipelines (Snowflake + Databricks + Airflow + dbt + Great Expectations), handling 8M+ records/month and 500K+ daily CDC updates. Delivered measurable reliability and efficiency gains (41% cost reduction, freshness improved from 4h to 30m, 35% fewer downstream incidents) and has experience building a lakehouse platform from scratch across 12 source systems.”

Abhinita Sanabada

Screened

Senior Software Engineer specializing in AI/ML systems and FinTech platforms

San Jose, CA9y exp

“Master’s student in Data Science at San Jose State University with prior software engineering experience at JPMorgan Chase and Zap Labs. She combines enterprise backend reliability work in financial systems with hands-on full-stack AI workflow projects, including a recruiting automation system built with React/Next.js, FastAPI/Node, Kafka, and WebSockets, with a strong emphasis on observability, human-in-the-loop controls, and maintainability.”

“Solutions engineering and technical consulting leader with experience across healthcare logistics, transportation SaaS, analytics, and HR interoperability. Stands out for deep integration and security expertise—spanning APIs, EDI, HL7/FHIR, SSO/SAML, and enterprise architecture—and for using customer-specific POVs and automation initiatives to drive adoption and measurable operational impact.”

“Full-stack engineer with healthcare and financial services experience who owned an end-to-end appointment scheduling feature at Norton Healthcare, improving scheduling speed and reliability through Angular/TypeScript, SQL Server, Entity Framework Core, Redis caching, and secure APIs. Also brings enterprise modernization experience from IBM and JPMorgan Chase, including microservices migration, CI/CD, and production observability.”

Vasudev Konde

Screened

Mid-level Full-Stack Java Developer specializing in APIs and cloud microservices

Phoenix, AZ5y exp

“AI/LLM engineer who has shipped a production document-intelligence agent that automated internal support workflows using RAG, tool calling, and robust fallback controls. Stands out for combining hands-on architecture with measurable business impact: 85% faster query resolution, 35% lower LLM cost, 40% fewer LLM calls, and enough automation to avoid adding 2-3 support engineers.”

“Full-stack engineer focused on high-throughput document/financial data platforms, building Angular/React front ends and Spring Boot microservices with Python/Flask services for heavy processing. Experienced in designing non-blocking, asynchronous workflows (Celery/RabbitMQ) and deploying containerized systems to AWS ECS with auto-scaling and CloudWatch monitoring.”

Harshal Sawant

Screened

Senior AI Engineer specializing in LLMs, RAG, and MLOps on multi-cloud

8y exp

“Built and productionized a secure internal RAG-based AI assistant (LangChain/FastAPI/FAISS on GCP), tackling real-world issues like latency, retrieval speed, and hallucinations—delivering 25% faster retrieval and 99.9% uptime. Also implemented scalable, reliable ML retraining orchestration with AWS Step Functions/SageMaker/Lambda and partners closely with compliance analysts to iteratively refine prompts and outputs to meet governance standards.”