Vetted Schema Validation Professionals

Rubeena Riyas

Screened References

Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms

7y exp

“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”

“Backend engineer with hands-on ownership of Python/Flask microservices and recommendation systems across edtech and telecom. Deployed and operated real-time personalization/recommendation platforms on AWS EKS with Jenkins-based CI/CD, GitOps-style declarative configs, and strong observability practices. Has migration experience moving legacy mixed environments to modern containerized Kubernetes and built Kafka pipelines feeding ML services while managing schema evolution.”

“AI engineer (2+ years; pursuing an online MS at UIUC) who has shipped an AI-powered voice screening platform end-to-end on GCP with strong production monitoring and measurable hiring-process impact (80% reduction in unqualified pass-through; ~50+ hours saved per role). Also built and deployed an AWS-based context-aware hybrid search system using OpenSearch as a vector store, and has hands-on experience with multi-agent LLM orchestration (ReAct) and structured-output guardrails.”

“Worked on onboarding a Middle East logistics client processing thousands of invoices/month, building a production-ready pipeline that routes known vendor PDFs to deterministic regex parsers via Tax ID matching and falls back to LlamaParse for unknown layouts. Added financial consistency validation plus human-in-the-loop review and logging/metrics to continuously reduce LLM usage and improve template coverage.”

“Zero Trust security product lead focused on UI/API delivery, stability, and customer adoption at enterprise scale, including deployments serving 1200 customers. Stands out for hands-on production debugging across the full stack, customer-facing incident ownership, and a pragmatic approach to turning failures into automated regression coverage.”

“At Esri, the candidate is building a production LLM-powered WebGIS AI framework that embeds an AI assistant into web maps and routes natural-language requests into ArcGIS JavaScript SDK functions via a LangGraph-orchestrated, multi-agent system. They emphasize production reliability and scale (strict tool calling/JSON, live schema validation, query guardrails) and rigorous evaluation/observability using LangSmith, offline prompt datasets, and latency/tool-call accuracy tracking.”

“Full-stack engineer with fintech and AI product experience: built HuddleAI end-to-end on Firebase/React, including a serverless LLM meeting-intelligence pipeline (FFmpeg + Google Speech-to-Text + GPT-4 with schema validation) and Slack notifications. At Gemini, owned a Postgres/Scala workflow change for wire deposit approvals that cut blocked registrations by 60% and emphasized correctness/compliance in UK/EU transaction-state UI.”

“Healthcare ML/AI engineer at Cigna who has owned a clinical RAG pipeline from prototype through production, monitoring, compliance, and iteration. Stands out for combining LLM product delivery with healthcare-grade safety and explainability, driving a 38% retrieval precision gain, 42% hallucination reduction, and meaningful improvements in team velocity and system reliability.”

“Interned at Paycom and shipped a productionized ML/AI system that automatically regenerates XPath selectors to self-heal Selenium UI tests when the DOM changes. The pipeline handled 1,000+ failing tests/hour with ~90–95% auto-fix accuracy, using confidence thresholds, human-in-the-loop fallbacks, logging/dashboards, and retraining loops to manage distribution shift and maintain reliability.”

“Backend/AI engineer who has built an offline, citation-grounded RAG system end-to-end with hybrid retrieval, local LLM inference, and quantitative evaluation via RAGAS. Also brings real-time systems experience from an Airbnb-like booking platform and data pipeline/ML quality work from a Bilibili internship, with a strong emphasis on reliability, privacy, and measurable correctness.”

“Data engineer who has owned end-to-end production pipelines ingesting ~500GB/day from APIs/databases/Kafka into an S3 data lake (Glue/Spark) with Airflow-orchestrated Great Expectations quality gates. Built resilient external data collection systems with idempotent jobs, exponential-backoff retries, raw data capture, and backfills; also shipped Snowflake-backed APIs with caching, versioned endpoints, and backward-compatible data contracts. Led an early-stage Azure data platform build with phased delivery and GitHub Actions CI/CD, resolving schema-mismatch incidents quickly without downstream corruption.”