Vetted Schema Validation Professionals

Chaitanya Annabathana

Screened

Mid-level Software Engineer specializing in AI pipelines and enterprise integrations

USA5y exp

“Candidate has 4 years of experience and appears strongest in customer-facing implementation and AI-enabled workflow automation. They describe owning deployments end-to-end, putting an LLM support assistant with RAG and function calling into production, and improving support operations with a 30% reduction in resolution time and 25% gain in agent productivity.”

Austin Pierce-Ptak

Screened

Mid-level Software Engineer specializing in full-stack and ETL systems

Seattle, WA5y exp

“Backend engineer with end-to-end ownership experience across enterprise SaaS and high-volume data systems, including PostgreSQL/.NET services at Visual Lease and ETL pipelines at Broadridge processing millions of records for Fortune 500 clients. Stands out for combining production support, observability thinking, and pragmatic architecture tradeoffs, while also experimenting with LLM-powered job application automation using Claude.”

“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”

“Full-stack engineer with strong ownership in fast-moving environments: designed and shipped a pre-order/campaign inventory system (NestJS + Strapi + Datadog) that freed 34% warehouse space and reduced stock risk to ~5.7%. Also built rapid, high-impact logistics features (Spot Sales) that drove last-mile cost to ~0 in ~40 days, and has hands-on AWS/Terraform/CI-CD experience including deploying a global RAG system with Pinecone, Datadog, and PagerDuty.”

“Backend/full-stack engineer with experience building internal government and banking platforms, including a branch operations management system at Bank of China (React/Apollo + Node GraphQL + MongoDB) and a capital project management service for the State of Alabama (Spring Boot + PostgreSQL). Emphasizes maintainable API design, strong validation/data integrity, RBAC/JWT security, and production reliability through clear service boundaries, logging, and monitoring.”

“Built and shipped a production LLM-powered customer support agent for an EV charging platform using RAG plus internal APIs, automating session/payment issues and ticket routing. Emphasizes production readiness via guardrails, schema validation, state-machine orchestration, monitoring, and continuous evals, delivering a reported 35–40% reduction in support tickets and improved customer satisfaction.”

Pooja Miryala

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for banking and healthcare

Ohio, USA4y exp

“Deployed a real-time LLM-driven call center summarization and agent-assist platform at Fifth Third Bank, combining transformer models (BERT/GPT) with FastAPI inference on AKS and vector storage (ChromaDB/PostgreSQL). Emphasizes production-grade reliability (autoscaling, CI/CD, monitoring) and measurable evaluation (A/B testing), and translates model outputs into business-facing Power BI insights for call center leadership.”

Sai somapalli

Screened

Senior LLM Engineer specializing in Generative AI, RAG, and multimodal assistants

USA6y exp

“GenAI/NLP engineer with experience building classification and summarization pipelines in PyTorch and deploying multimodal GPT-4-style workflows. Has integrated LLM applications across OpenAI, Azure OpenAI, and Amazon Bedrock, and uses LangChain/LlamaIndex/Semantic Kernel to orchestrate RAG and agent workflows with production-focused evaluation metrics like task success rate and groundedness.”

Nilesh Bhoi

Screened

Mid-Level Software Engineer specializing in full-stack web apps and real-time systems

Austin, TX3y exp

“Software engineer who has owned and improved a customer-facing quote flow in a Vue/Nuxt app, using production observability to reduce latency and improve reliability via caching and request-handling fixes. Also shipped an internal LLM Q&A tool using embeddings + RAG over approved company docs and past support tickets, with guardrails, logging, and an evaluation loop that drove retrieval/prompt improvements. Seeking ~$110k base and requires H1B transfer sponsorship.”

Sreedivya Nagalli

Screened

Junior AI/ML Engineer specializing in deep learning and full-stack ML applications

2y exp

“Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.”

Sravya Chunduri

Screened

Mid-level AI/ML Engineer specializing in LLM, NLP, and MLOps

Virginia, USA4y exp

“AI/ML Engineer with 3+ years of experience spanning RAG pipelines, MLOps, large-scale data workflow automation, and resilient Playwright-based UI automation. At Black Hawk Network and Wipro, they describe shipping production systems with strong observability and compliance controls, including reducing flaky automation failures from 30% to under 2% and automating 3+ TB/day reconciliation workflows.”

“Application-focused full-stack engineer in the clinical/health domain who shipped an LLM-powered clinical note summarization workflow end-to-end (FastAPI + Postgres + Kafka workers + React/TypeScript UI) with strong attention to security, auditability, and clinician trust. Has hands-on AWS/EKS operations experience and has resolved real production latency/scaling issues through async processing, query/index tuning, caching, and horizontal scaling.”

“Hands-on engineer focused on productionizing LLM-powered assistants: builds RAG pipelines with guardrails, response schemas, and citation-grounded outputs, then hardens them with explicit NFRs (latency, uptime, security, cost). Experienced diagnosing agentic/LLM workflow issues in real time using observability and stepwise isolation, and supports go-to-market via developer demos, workshops, and pre-sales technical evaluations in microservices/Spring Boot environments.”

“Backend/DevX engineer with startup experience who built internal JavaScript SDKs for POS integrations, including a daily GMV calculation feature standardized across multiple POS providers. Strong in performance debugging (used Jaeger to resolve legacy WebSocket bottlenecks) and developer enablement—built a cronjob migration tool (ArgoWorkflow to internal platform) with documentation that let teams migrate in ~30 seconds, plus handled on-call and internal support.”

“Full-stack engineer with Deloitte and CrowdDoing experience shipping production web platforms on AWS (EC2/RDS/S3/Fargate) using React/TypeScript and Node/Express/PostgreSQL. Built customer-facing authentication/SSO flows (OAuth2 + JWT) and state-specific US privacy consent workflows, and also delivered a Python/Flask LLM-based finance document parser chatbot with vector DB integration and latency optimizations.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

“Built and shipped an AI-driven interview evaluation pipeline at SeekOut that automated recruiter screening via a multi-stage LLM agent workflow (.NET backend, RabbitMQ orchestration, Python workers). Emphasizes production-grade reliability (idempotency, retries, strict JSON/schema validation), strong observability with OpenTelemetry, and measurable efficiency gains including ~40% reduction in token usage/cost.”

“Data engineer in healthcare/clinical data platforms (HarmonCare) who built and operated an end-to-end lakehouse pipeline ingesting HL7/FHIR at ~2–3M records/day on AWS (Glue/Lambda/S3/Spark) and serving trusted datasets in Snowflake. Implemented strong validation/reconciliation gates and a data quality framework that reduced discrepancies ~40%, plus CI/CD (GitHub Actions/Terraform) and monitoring (Airflow/CloudWatch).”

Sabita Kumari

Screened

Senior Full-Stack AI Engineer specializing in LLM/RAG agentic systems

Boston, MA11y exp

“Built and deployed JobMatcher AI, an LLM-driven workflow automation product for job seekers that extracts requirements from job descriptions, matches to user skills, and generates tailored outreach. Demonstrated strong production engineering by cutting per-run cost ~70%, improving reliability with retries/backoff/fallbacks, and reducing hallucinations via schema validation and templating; also orchestrated the system with LangGraph plus Docker Compose across API, vector DB, and workers.”

Vivekananda Reddy

Screened

Mid-Level Full-Stack Software Engineer specializing in Java, React, and AWS

Dallas, TX4y exp

“Backend engineer focused on cloud-native microservices on AWS, owning Python/Flask ingestion services integrated with S3/Lambda and deployed via Docker/Kubernetes with CI/CD. Has led phased migrations from manually managed EC2 setups to automated CloudFormation + pipeline-driven releases, and designed event-driven near-real-time pipelines with idempotency, retry/backoff, and strong observability.”

Shrinivas Bhusannavar

Screened

Mid-level AI Engineer specializing in agentic LLM systems and RAG platforms

San Jose, CA5y exp

“Built and shipped Serrano AI, a multi-tenant SaaS conversational AI platform that automates Odoo ERP workflows and lets ops/finance/supply-chain teams query ERP data in natural language. Implemented a multi-agent architecture (LangChain/LangGraph/CrewAI) with hybrid RAG over ERP schemas, deployed on Heroku/Vercel with production observability, cutting reporting time by ~80% while addressing hallucinations, latency, and schema complexity.”

Bhavishyasai Chigurupati

Screened

Mid-Level Data/ML Engineer specializing in Generative AI and cloud data platforms

Overland Park, KS5y exp

“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”

Xin Li

Screened

Staff Software Engineer specializing in distributed systems and high-concurrency APIs

Santa Clara, CA17y exp

“Full-stack engineer in an insurance + telematics org who built a 0→1 multi-agent, AI-driven mobile regression execution system integrating Jira and n8n, emphasizing determinism (schema-validated outputs), traceability (state-machine style step archives), and scalable device scheduling. Also designed an event-driven microservices architecture using Kafka with standardized API/event contracts and has hands-on incident response experience improving resilience via rate limiting, circuit breaking, caching, and idempotency.”

Kavya Myakala

Screened

Mid-level Frontend Developer specializing in security analytics dashboards

NJ, USA4y exp

“Built and shipped production LLM agents including an end-to-end customer support resolution system (99.9% uptime target) that improved customer satisfaction by ~18% and reduced the need to scale support headcount. Demonstrates strong agent engineering fundamentals—tool-based orchestration, schema-first structured outputs with deterministic validation, and robust eval/monitoring loops—plus experience integrating agents with messy ERP data using canonical normalization and safe fallbacks.”