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Vetted Schema Validation Professionals
Pre-screened and vetted.
Senior Full-Stack Java Engineer specializing in cloud-native event-driven systems
Mid-Level Full-Stack Java Developer specializing in microservices and real-time data systems
Junior Data Engineer specializing in cloud data pipelines and LLM/RAG systems
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Mid-level Data Engineer specializing in lakehouse and cloud data platforms
Senior Software Engineer specializing in full-stack, data, and ML systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps in financial services
Mid-level Full-Stack Developer specializing in React, Spring Boot, and cloud microservices
Senior Data Scientist / ML Engineer specializing in computer vision and production ML systems
Mid-level Data Engineer specializing in scalable batch/streaming pipelines and cloud data platforms
Junior Full-Stack Engineer specializing in React, Node.js, and event-driven systems
Mid-level Data Engineer specializing in real-time streaming and ML feature pipelines
Mid-level Software Engineer specializing in full-stack and cloud data platforms
Senior Backend Engineer specializing in telemetry, APIs, and cloud-native systems
Mid-level AI & Machine Learning Engineer specializing in agentic workflows and RAG/GraphRAG
Senior Software Engineer specializing in distributed data platforms and GenAI automation in BFSI
Staff-level AI/ML Engineer specializing in enterprise RAG, agentic automation, and AI governance
Mid-level Customer Success Engineer specializing in application security and FinTech integrations
Rafael Ortega
Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems
“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”
Pavan Kalyan
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”