Vetted Schema Validation Professionals

“Data/Backend engineer with experience at Naukri building large-scale analytics products over a 130M+ user base, including Spark/Airflow pipelines and Kafka-based clickstream validation with Confluent Schema Registry. Also built an audience segmentation backend (Athena/S3 + Spring Boot APIs) for non-technical internal teams and recently shipped a GenAI customer data audit system (FastAPI/Postgres/Llama) that cut sales-planning validation from ~3 months to ~1 week.”

Harsh Sanas

Screened

Intern-level Software Engineer specializing in GenAI, RAG, and backend systems

San Francisco, CA2y exp

“AI/LLM engineer focused on shipping production-grade agents that automate support, sales intake, and ERP-connected workflows. Stands out for combining strong orchestration and guardrails with measurable business outcomes, including 45% faster support handling, ~$1.2M annual savings, 18% higher customer satisfaction, and 99.5%+ reliability in production.”

“CMU candidate with hands-on experience taking LLM concepts from research prototypes toward production-ready designs (structured outputs, guardrails, failure-scenario evaluation). Also partnered with sales/customer teams at Mazecare to drive adoption with Dontia Alliance (largest dental clinic chain in Singapore) and engaged Singapore government stakeholders, bridging clinical workflow needs with IT security/integration concerns.”

“JavaScript/React-focused engineer with meaningful open-source impact: redesigned cache key normalization for a client-side data fetching/caching library using deterministic hashing, added robust test coverage, and collaborated closely with maintainers through GitHub PRs/issues. Also drives measurable runtime improvements by profiling hot paths, refactoring core abstractions, and validating with benchmarks/load tests; has taken ownership of unowned initiatives like improving relevance/ranking in an internal search platform.”

“Full-stack AI/LLM engineer who has deployed a production LLM backend (Mistral 14B) on GKE to auto-transform datasets and generate runnable ML training pipelines, addressing hallucinations, schema mismatch, latency, and burst scaling with caching/prompt compression and HPA. Also has internship experience (Splunk, BlackOffer) delivering data automation and 10+ Power BI dashboards for non-technical stakeholders with measurable efficiency gains.”

“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”

Arun Gampala

Screened

Mid-level Full-Stack Developer specializing in MERN and AWS microservices

TX, USA4y exp

“Backend engineer with experience at MetLife and Amazon focused on security and control for internal and customer-facing services. Emphasizes contract-first Python/FastAPI APIs with strong auth (JWT + RBAC/claims), data-layer isolation (RLS/tenant scoping), and reliability practices like incremental refactors, rollback planning, and idempotency to handle retry-driven failure modes.”

Kunal Singh Pundir

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and GenAI systems

USA, USA5y exp

“Built and owned an end-to-end AI-driven decisioning platform at Uber, combining LLM orchestration with typed tool contracts and a Snowflake-based RAG pipeline to make decisions fully auditable. Delivered large-scale measurable impact (120k requests/day, 18k cases auto-resolved/month) while improving ops SLA from 3 days to 6 hours and cutting incident response time nearly in half. Previously led a high-risk strangler-fig modernization of a legacy insurance platform across 120+ microsites at Accenture, coordinating across multiple squads with feature-flagged parallel cutovers.”

“End-to-end deployment owner for Oracle document delivery/print services in a hospital-like production environment, focused on reliability/performance at scale (thousands of systems). Also describes implementing event-driven RAG/agentic LLM workflows with attention to embeddings/index consistency, latency, and measurable improvements in response relevance and operational efficiency.”

“Built Baioniq, an enterprise LLM platform for automating extraction from massive unstructured documents like contracts and insurance claims. They demonstrate unusually strong production depth in agentic AI—scaling to 100k+ requests/day, processing 1M+ claim documents, and improving extraction accuracy through rigorous RAG architecture, evaluation, and fallback design.”

“Built and launched a production self-healing MLOps agent that autonomously diagnosed and fixed model training failures on Kubernetes GPU infrastructure. Combines deep AI infrastructure knowledge with full-stack product ownership, and has delivered measurable impact including 35% less infrastructure waste, nearly 50% less troubleshooting time, and 60% lower LLM API costs.”

“Built AI-powered natural language search and summarization features for internal financial platforms at JPMorgan, with a strong focus on trust, compliance, auditability, and failure handling. Stands out for treating AI as one component in a larger enterprise system rather than a magic layer, and for combining hands-on LLM integration experience with thoughtful agent architecture and validation design.”

“Full-stack engineer with hands-on ownership of a customer-facing advanced performance metrics experience in the Amazon S3 console, spanning React UI, Python/Node services, Redshift/RDS data access, and AWS IaC/CI-CD with CloudWatch/Route53 operational readiness. Demonstrates strong production instincts around resilience (partial failures, multi-region inconsistencies), progressive rollouts/feature flags, and reliable ETL/integration patterns (idempotency, backfills, reconciliation).”

Kiran Kumar

Screened

Mid-level Software Engineer specializing in Java microservices and GenAI automation

USA4y exp

“Software engineer (4+ years) with hands-on production GenAI experience: built an AI incident triage assistant that summarizes production logs for on-call engineers and iterated it using real incident metrics (time-to-signal, triage duration). Also shipped a RAG-based customer support knowledge assistant using embeddings + vector retrieval with strong guardrails (relevance thresholds/abstain, sanitization, auditing) and a formal eval loop (500-query gold set) that drove measurable retrieval improvements.”

“Software engineer focused on reliability and scalable systems: built React/TypeScript dashboards backed by Java/Spring Boot APIs and designed Kafka-based microservices with strong contract/versioning discipline. Known for shipping incremental improvements with tight feedback loops and for creating internal observability tools that streamline on-call and incident diagnosis under high-traffic conditions.”

“Backend/AI workflow engineer with PayPal experience building workflow-driven financial compliance systems (Python/Java, Postgres, AWS/EKS) at thousands of executions/day. Has shipped production LLM-powered document extraction with strict schema/rule validation, auditability, and human-in-the-loop fallbacks, and has deep expertise in reliability (idempotency, locking, state machines) and Postgres performance tuning.”

“Built and shipped a production enterprise RAG knowledge assistant that returns grounded, cited answers and uses confidence-based fallbacks (clarifying questions/abstention) with monitoring and compliance controls for sensitive data. Implemented end-to-end agent orchestration (function calling, structured JSON, state, retries/rate limits) plus eval/feedback loops, and achieved a reported 30–40% improvement in knowledge-task completion time while reducing hallucinations via retrieval improvements.”

Vidhi Upadhyay

Screened

Senior Software Engineer specializing in AI/ML, computer vision, and cloud-native systems

Remote8y exp

“Independently built a production-grade, containerized enterprise agentic AI platform (stateful orchestration + RAG) focused on real-world reliability—guardrails, citation-based outputs, reranking, query rewriting, and evaluation harnesses to reduce hallucinations. Hands-on with OpenAI SDK, CrewAI, and LangGraph, and has delivered AI solutions for non-technical NGO stakeholders via demos and practical POCs.”

Bhanu Chander

Screened

Senior Data Engineer specializing in cloud data platforms and real-time pipelines

New York, NY6y exp

“Data engineer focused on reliability and observability, building end-to-end pipelines processing millions of records/day from sources like S3 and Kafka. Has hands-on experience with Airflow-based data quality automation, PySpark/Databricks transformations, and shipping versioned Python REST APIs deployed via Docker/Kubernetes with CI/CD (Jenkins) and monitoring (CloudWatch/Azure Logs).”

“Data engineer with experience at Expedia building SQL Server and Azure Data Factory pipelines for business reporting and analytics. Stands out for pragmatic end-to-end pipeline ownership in ambiguous environments, with a strong emphasis on data quality, rerunnability, query performance, and making downstream datasets reliable for other teams.”

“Backend/data engineer focused on compliance and statistical processing systems on AWS, building containerized FastAPI services plus event-driven async workflows (Step Functions/EventBridge) with strong reliability patterns (JWT auth, idempotency, structured logging). Has modernized SAS-based batch pipelines into modular Python/AWS services with parallel-run parity validation, and has demonstrated measurable SQL performance wins (40+ min to <10 min) and hands-on incident ownership using CloudWatch-driven detection and prevention.”

“Backend-focused full-stack engineer who built an MVP at Neon AI for PhD students: a FastAPI backend integrating multiple cloud and local LLMs plus a RAG pipeline with session/identity management, designed to be modular and extensible across domains. Also has VMware experience debugging production issues and executing safe, API-compatible refactors with staged rollouts and strong security controls.”

“AI/full-stack engineer with experience delivering production-critical healthcare AI at Roche (digital pathology imaging platform using Microsoft Gigapath for cancer diagnosis) and building scalable LLM-backed products. Strong in designing async AI backends (Django/Celery/Postgres/Redis on GCP), reliability engineering (Datadog, incident response), and agent-style document analysis workflows with evaluation loops.”