Vetted Data Ingestion Professionals

“Enterprise cloud/SaaS customer success leader who owned a strategic $102M Azure commitment end-to-end, rescuing a Kubernetes migration by formalizing Sev A/B triage/escalation ownership and executive governance. Drove measurable outcomes including restored executive confidence and consumption growth from $1M to $2M/month (~30% adoption growth) by tying stabilization milestones to migration velocity, forecasting, and expansion strategy.”

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Built a production LLM-powered data ingestion workflow at Provi, an online alcohol marketplace, to clean and match millions of distributor inventory items against a product catalog. Their experience is strongest in applying LLMs to real-world, large-scale data operations with AWS Glue, S3, batching, API integration, human review, and drift detection.”

“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.”

“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”

“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.”

“Backend/AI engineer who built a production GPU-backed real-time inference API at Nvidia and debugged burst-induced tail latency, cutting P95 by ~29% through dynamic batching and backpressure. Also shipped an end-to-end RAG + agentic operational diagnostics assistant with strict tool controls, evidence citation, confidence gating, and strong production guardrails, plus demonstrated hands-on Postgres optimization (900ms to 40–60ms).”

“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.”

Pratham Thukral

Screened

Mid-level Software Engineer specializing in distributed systems on AWS

Seattle, WA3y exp

“Data/infra engineer with AWS DynamoDB experience who has shipped reliability-critical systems (Global Tables replica repair protocol) and customer-facing service rollouts using canary/percentage-based deployments, strong observability, and rollback strategies. Also built end-to-end Airflow pipelines producing weekly automated reports over ~10TB of advertising segment data, with rigorous week-over-week data quality validation.”

“CMU engineer with hands-on experience building full-stack AI products across internships and academic projects, including a Microsoft real-time multimodal assistant and a Cursor-like coding agent. Stands out for combining low-latency systems engineering with strong product instincts, shipping AI tools that improved latency by 35%, cut grading time from five days to under 10 minutes, and reduced user resubmissions by 45%.”