Vetted Regression Testing Professionals

“Built a production LLM-powered root cause analysis agent for supply chain alerts that helped operations managers avoid manual dashboard investigation. Demonstrates unusually strong depth in agent reliability, orchestration, and observability, with concrete production practices like hallucination blocking, shadow testing on 500 cases, and data-driven improvements that raised user agreement to 94% while cutting GPT-4 usage by 60%.”

Vinay Ramrupe

Screened

Mid AI/ML Engineer specializing in LLM and enterprise generative AI

San Francisco, CA5y exp

“ML/AI engineer focused on taking LLM systems from experimentation to reliable production, including enterprise copilot and RAG-based knowledge retrieval use cases. Stands out for combining data pipelines, model training, inference optimization, automated evaluation, and safety guardrails, with cited impact including 20% throughput gains and 30% less manual evaluation effort.”

UDAY JARAJAPU

Screened

Executive engineering leader specializing in AI-native products and large-scale platforms

Fremont, CA22y exp

“Experienced cross-functional operator with background in AI, edtech, consumer mobile, cloud, and real estate search, including roles at Apple, AWS, and Trulia. Currently building Typerighter, an AI-native writing workspace focused on compliance, authenticity, and human-verifiable content, with a nuanced understanding of institutional requirements and startup/accelerator dynamics.”

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.”

“AWS AI Lab engineer who deployed a production Chain-of-Thought analytical agent for tabular reasoning, emphasizing grounded tool-constrained workflows with schema-validated intermediate outputs. Built robust evaluation/logging with step-level observability to catch regressions across model versions, and has experience scaling distributed LLM training via Slurm + DeepSpeed/FSDP with checkpointing and failure recovery.”