Vetted Data Governance Professionals

“Backend/data engineer with production experience building FastAPI services with strong reliability patterns (circuit breaker, rate limiting, caching, graceful degradation) and JWT/OAuth2 auth. Has delivered AWS EKS deployments via Terraform with Secrets Manager/IRSA and HPA autoscaling, and built Glue/Spark ETL pipelines on S3 Parquet with schema-evolution and idempotent reruns; also demonstrated measurable SQL tuning impact (20–30s to <10s).”

“Built and productionized enterprise LLM/RAG systems, including a Boeing internal solution that gave 400+ program managers conversational access to 1M+ rows of schedule data, with strong emphasis on governance, reliability, and reducing hallucinations in tabular domains. Also has experience running developer-focused workshops (UC Berkeley computer architecture) and partnering with customer-facing stakeholders to drive adoption of a compliance-sensitive NLP product (SEC-aligned) at Penserra.”

“Data engineer at Microsoft who owned an end-to-end subscription analytics platform processing 7TB+ daily across 40+ pipelines, combining ADF batch ingestion with Kafka/Spark streaming and rigorous Great Expectations quality gates. Built a Fabric-based self-service ingestion platform with CI/CD and observability, plus a Databricks feature store serving near-real-time ML inference with Delta Lake reliability and versioning.”

Asrith Velireddy

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems

Harrison, NJ4y exp

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

“Built GenMedX, a multi-module clinical AI system for emergency department decision support spanning triage prediction, diagnosis, medication Q&A, and visit summarization. Stands out for combining medical LLM fine-tuning, RAG, and rigorous evaluation/monitoring to drive a major triage recall improvement from 38.5% to 76.6%, with a strong focus on safety, edge-case detection, and production reliability.”

“Architect/engineering leader who built a multi-tenant AI platform end-to-end, including a secure FastAPI orchestrator (JWT, RBAC, tenant isolation, auditing) and an extensible MCP tool-routing layer, then productionized it via fully containerized microservices (Docker, Postgres/pgvector, Redis). Also has strong governance and compliance experience (ARB with security/privacy/SOX) and has owned high-severity incidents through mitigation and RCA/RCCA, plus prior high-volume payments/accounting data pipeline design with audit-grade integrity checks.”

Jay Desai

Screened

Executive Technology Architect specializing in FinTech, AI platforms, and cloud modernization

California, USA24y exp

“Entrepreneur building an early-stage community food-sharing, network-effects platform positioned as an alternative to restaurant-centric delivery models (e.g., DoorDash/Uber Eats) by enabling homeowners to act as micro-entrepreneurs. Has been piloting a local community program for nearly a year and uses experiments and customer feedback to validate hypotheses and assess scalability.”

“Current financial data analyst at Maximus with hands-on experience building SQL and Python reporting pipelines for expenditure, refund, and retention analysis. Stands out for turning messy multi-source financial data into trusted dashboards, automating reporting to save 15+ hours weekly, and driving measurable reductions in refund rates through cohort-based analysis.”