Vetted Unit Testing Professionals

Christian Alcala

Screened

Mid-level Software Engineer specializing in LLM-powered analytics

Redwood Shores, CA4y exp

“Engineer with a pragmatic, production-focused approach to AI development, emphasizing verification, observability, and system design over hype. Built LLM-driven features and automated regression/validation pipelines, including quality measurement work at Oracle, and uses hands-on projects to test how AI fits into real business workflows.”

Shuvam Mitra

Screened

Mid-level Data Scientist specializing in anomaly detection and production ML

Pittsburgh, PA4y exp

“Interned at Backblaze building production AI systems for incident response and security operations, including an internal LLM-powered incident triage assistant that used Snowflake + RAG over historical tickets/postmortems and delivered results via Slack and a web UI. Emphasizes reliability (PII filtering, grounding, schema validation, fallbacks) and rigorous evaluation/observability (offline replay, partial rollouts, time-to-first-action metrics, Prometheus/Grafana).”

Amrita Pritam

Screened

Senior Backend Engineer specializing in distributed microservices and event-driven systems

Fremont, CA10y exp

“Backend engineer with production experience building a high-scale notification pipeline (~20M/day) using Java/Dropwizard with Kafka and Azure Queue, including DLQ/poison-message handling and the outbox pattern for reliability. Also led a batch-based migration of Yammer Messaging user data from PostgreSQL to Azure Cosmos DB for global multi-region scale, addressing throttling and network failures via retries, escalation policies, and dynamic throughput tuning.”

“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”

“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”

“Full-stack engineer with Uber experience building finance/billing reconciliation systems: shipped and owned an internal operations dashboard (Next.js App Router/TypeScript) that cut investigation time from hours to minutes and improved load time from ~6–7s to <2s. Deep in Postgres modeling and performance (sub-200ms optimized queries) plus durable event-driven workflow orchestration with idempotency, retries/backoff, DLQs, and reconciliation jobs; also has seed-to-Series C startup experience emphasizing end-to-end ownership.”

“Software engineer at Intel who owns a production Go/Kubernetes backend for supply-chain transparency and end-to-end hardware integrity verification in a hybrid cloud setup (AWS control plane + Azure data plane). Also built and shipped an AI agent workflow for real-estate due diligence that turns raw Excel spreadsheets into structured investment outputs and auto-generated PowerPoint insights using LangGraph, with strong emphasis on verification, observability, and reliability guardrails.”

“Data/analytics engineer who stood up foundational pipelines and services at Meta for the Ray-Ban Meta launch—building a retailer sales ingestion system (S3/Hive) with rigorous DQ checks, 1-day SLAs, and dimensional rollups used by GTM to track sales trends. Also built a modular multi-retailer web-scraping system for out-of-stock alerts and shipped internal GraphQL APIs and an n8n-like workflow builder using serverless (AWS Lambda) with strong testing and observability practices.”