Vetted Schema Validation Professionals

Prudhvi Avula

Screened

Senior Full-Stack Software Engineer specializing in Java, React, and distributed systems

Austin, TX7y exp

“Implementation and AI workflow engineer who has owned customer deployments end-to-end for messaging/integration platforms and also built production-oriented LLM systems. Stands out for combining stakeholder-facing delivery leadership with hands-on experience in Kafka/MongoDB integrations, RAG/agent architectures, and resilient document-processing pipelines with strong validation and fallback controls.”

“Backend/Node.js-focused engineer who improved a widely used shared config/logging utility library by fixing a real-world async race condition (single disk read under concurrency) and adding stronger validation/testing, resulting in more deterministic services and faster startup/build/CI times. Also builds internal platform automation spanning Python/Go/TypeScript with strong documentation practices and security-conscious customer onboarding (e.g., sensitive Kubernetes clusters, HashiCorp Vault access issues).”

“Backend/data platform engineer who owned an enterprise Django REST + PostgreSQL reporting backend and built Python ETL pipelines to normalize 3M+ legacy customer records, improving data reliability by 85%. Strong Kubernetes/GitOps practitioner (Helm, ArgoCD, Jenkins/GitHub Actions) with real-world production debugging experience, plus Kafka streaming at 5M events/day and a zero-downtime monolith-to-event-driven microservices migration on AWS that cut infra costs by 42%.”

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

Sai Kumar K

Screened

Mid-Level Frontend Engineer specializing in legal data platforms

5y exp

“Built and operated end-to-end legal-document data pipelines fed by hundreds of scraper sources, emphasizing data quality validation, reliability (CloudWatch monitoring/alerting, retries, backfills), and serving enriched legal data via serverless AWS APIs (Lambda/API Gateway). Experienced in keeping API contracts stable with additive versioning practices and shipping MVPs quickly with CI/CD and observability in place.”

“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”

“Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).”

“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”

varsha viswanathan

Screened ReferencesStrong rec.

Entry-Level Software Engineer specializing in backend systems and FinTech

Fremont, CA1y exp

“Software engineering intern experience at Zoho Corp and Zeus Desk building and deploying customer-facing systems. Delivered a real-time booking platform backend that stayed stable for 1,000+ users by optimizing MySQL queries/indexing and shipping hotfixes during production latency incidents. Also integrated financial operations APIs across 50+ small-bank partners by creating a normalization/validation layer to handle inconsistent partner data and prevent integration breakages.”

Naveen Chokkakula

Screened References

Senior Golang Engineer specializing in distributed systems and cloud-native microservices

Texas, USA5y exp

“Software engineer with experience at UWorld and Infosys, focused on full-stack product delivery and backend reliability. Stands out for owning analytics/onboarding experiences end-to-end while also building resilient distributed systems, reusable service primitives, and safe multi-tenant configuration guardrails.”

“Built and continuously ships AI features for a recipe website, including a production workflow that ingests Instagram reels via webhooks, extracts/transcribes content, and converts it into structured recipe data. Also brings enterprise async/backend experience from Dot Foods, where they work with Azure Service Bus, Cosmos DB, and a pre-production Teams bot for logistics stakeholder questions.”

Nidhi Sharma

Screened ReferencesStrong rec.

Junior Software Engineer specializing in backend systems, AI, and cloud infrastructure

Noida, India2y exp

“Built multiple AI-heavy systems with a strong engineering lens on observability, reliability, and real-world usability, including an LLM gateway for auditability/failure isolation and Allyvision, an accessibility tool for visually impaired users. Also owned an end-to-end warehouse shipment tracking dashboard at Addverb Technologies that drove measurable operational gains, combining backend/data depth with frontend product execution.”

“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”

“Backend/data engineer who built an enterprise LLM assistant (AI Genie) at Broadband Insights using a LangChain + GPT-4 + Pinecone RAG pipeline to automate broadband analytics reporting. Developed Python/Dagster ETL processing 10M+ records/day and improved data freshness by 60%, with production-grade scalability patterns (async workers, containerized microservices, Kubernetes) and strong multi-tenant isolation practices.”

LAKSHMI KATTAMURI

Screened

Mid-level Embedded Software Engineer specializing in RTOS and microcontroller firmware

Boston, MA4y exp

“Backend/embedded-focused engineer with hands-on experience designing real-time, memory/power-constrained firmware architectures and also building Python/FastAPI services. Demonstrates strong production rigor across migrations (strangler pattern, shadow reads, feature flags), security (OIDC/JWT, RBAC/ABAC, Postgres RLS), and robustness for async workflows (idempotency, event ordering, state machines).”

“Backend/platform engineer with strong ownership of production systems, including a full Azure migration from a VM-based monolith to a containerized, event-driven microservices architecture. They combine cloud infrastructure, LLM/RAG optimization, and pragmatic stakeholder management, with measurable wins including 90% infra cost reduction, faster deployments, and significantly improved latency and token efficiency.”

Sasi Kanduri

Screened

Senior Full-Stack Software Engineer specializing in backend systems and workflow platforms

Sacramento, CA7y exp

“Full-stack engineer with strong React and Python backend depth who has owned complex analytical products end-to-end, from performant UIs to FastAPI services, SQLAlchemy data models, Redis caching, and production observability. Particularly compelling is their 0→1 automation work in the water systems domain, where they built Airflow- and LLM-powered workflows that reduced manual notification and correction work by 90%.”