Vetted Data Engineers in the Bay Area

Suraj Thangellapally

Screened

Junior Software Engineer specializing in machine learning and data science

San Jose, CA2y exp

“Python backend engineer who built a personal LLM-powered AI code review tool that parses code into context-preserving diff chunks and uses the OpenAI API to analyze and summarize changes. Has hands-on Kubernetes deployment experience (replicas, rolling updates, ConfigMaps/Secrets, health probes) and follows GitOps-style, declarative CI/CD workflows; also has experience designing streaming/event-style processing with attention to reliability and observability.”

Tejas Kolpek

Screened

Mid-level Solutions Architect/Engineer specializing in AI and data integrations

Mountain View, CA5y exp

“Solutions Engineer specializing in taking LLM copilots from demo to production, with a strong emphasis on enterprise security (RBAC/OAuth), grounded RAG behavior (cite-or-refuse), and operational readiness (eval loops, logging, runbooks). Experienced in real-time diagnosis of agentic/LLM workflow failures and in partnering with Sales/CS to run integration-first POCs that clear security and reliability concerns and accelerate rollout.”

Shabari Vignesh

Screened

Mid-level Data Engineer specializing in cloud data platforms and AI agents

Santa Clara, CA6y exp

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

Lakshmi Priya Ramisetty

Screened

Mid-level ML & Data Engineer specializing in GenAI, graph modeling, and fraud/risk analytics

Redwood City, CA5y exp

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”