Vetted Alerting Professionals

Padmaja Tyada

Screened ReferencesStrong rec.

Executive engineering leader specializing in cloud, AI, and FinTech platforms

San Jose, CA25y exp

“Engineering leader with experience scaling teams from 25 to 60+ and delivering both enterprise platform modernization and AI-native product innovation. At Experian, he led an AWS transformation of a high-volume credit platform serving 250M+ consumers, achieving 10x performance gains and 30% cost reduction; at Channel Core, he built AI-powered creator tools using OpenAI and RAG that improved adoption and personalized outreach outcomes.”

“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”

Chavez Yi Wei Cheong

Screened

Mid-level analyst and software engineer specializing in AI, distributed systems, and product delivery

San Francisco, CA5y exp

“Engineer with end-to-end shipping experience spanning large-scale infrastructure at Meta and scrappy startup automation work. Built a C++ streaming platform for Facebook Marketplace with canary and chaos testing, and also created Selenium-based scraping and GTM automation workflows under ambiguous, real-world conditions.”

Thazim Banu Shaffee

Screened ReferencesStrong rec.

Engineering Manager specializing in payments, risk, and high-scale distributed systems

San Jose, CA14y exp

“Engineering leader/player-coach on a risk core transaction platform (payments/branded checkout) who led major migrations from a monolithic stack to microservices, including API contract redesign and performance improvements (reported ~500ms latency reduction). Experienced running high-stakes production incidents (upgrade-related outage/degradation) end-to-end with RCA and rollout-process changes, and has accelerated delivery via documentation/tooling (audit sign-off cycle reduced from ~3 sprints to ~1).”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

“Fitbit engineer who has taken multiple projects from concept to release, including architecting a new warranty-evaluation system that achieved 100% accuracy and saved the company $6M. Interested in exploring startup ideas and emphasizes mission alignment and building strong cross-functional teams.”

Sergey Pustovit

Screened

Director-level Data Platform & Analytics Engineering Leader specializing in distributed systems

Irvine, CA31y exp

“Entrepreneurially minded builder focused on proving architecture concepts via minimal demo prototypes for marketing. Has hands-on experience improving an A/B experimentation framework by interviewing stakeholders, identifying system limits and bottlenecks, and defining success criteria to scale experimentation and speed up analysis.”

“Fintech and AI product engineer who has owned major production rollouts, including Lending Club's banking-arm launch, and has since built LLM-powered decision systems for finance and climate use cases. Particularly strong in combining stakeholder management with pragmatic architecture choices like observability, deterministic pipeline design, RAG, and document-to-structured-data workflows.”

“Data engineer focused on building and operating reliable Airflow-orchestrated pipelines into BigQuery, including daily billing ingestion (~1GB/day) and ad platform (Facebook/LinkedIn) data collection. Implemented end-to-end data quality checks plus org-wide incident response automation integrating PagerDuty, Slack, and Jira, and has experience executing large backfills (4–5TB) via time-window batching.”