Vetted Monitoring Professionals

Patrick Jose

Screened

Senior AI Engineer specializing in LLM applications and full-stack systems

San Francisco, CA8y exp

“Built and owned a production LLM/RAG customer support assistant end-to-end, from prototype through deployment, monitoring, and iteration. Their work automated roughly 40% of common support queries and cut response times by about 30%, while also creating reusable Python inference services that improved consistency and team velocity.”

“Entrepreneurially driven technical leader seeking to partner with a founder/business plan owner to provide technical expertise. Helped drive Wiser's expansion into Europe by evaluating acquisition targets' technical estates and making the recommendation that was chosen. Applied lean, high-leverage product thinking at Nabis on a two-sided marketplace, delivering buyer value with a simple algorithm and later adding paid boosting for brands.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

Keerthana Senthilnathan

Screened

Junior Machine Learning Engineer specializing in LLM systems and inference reliability

California, USA1y exp

“ML/LLM infrastructure-focused engineer who built a production stateful LLM inference service that cuts latency and GPU compute for repeated/overlapping prompts via caching with correctness guardrails. Strong in Kubernetes-based deployment and reliability engineering, using A/B testing and similarity-based evaluation to quantify performance gains without sacrificing output quality.”

“Amazon Prime Video backend engineer who built and operated high-traffic Python/FastAPI services and AWS-native data/batch systems. Demonstrates strong production reliability and incident ownership (CloudWatch/X-Ray), plus measurable performance wins (8s to <200ms query latency, ~40% CPU reduction) and cost-focused architectures (Lambda + ECS/Fargate with Fargate Spot).”

“Engineering leader in cloud security at SysTech with player-coach experience spanning cross-team data/ownership standardization and reporting platform user-journey improvements. Stays technically deep through observability (SLA/SLOs, dashboards, alerting), rigorous code reviews (including AI-assisted coding), and end-to-end incident ownership in IAM/agentless cloud event collection. Targeting $270K–$300K base plus bonus/equity.”

Shady Ashkar

Screened

Executive Technology & Engineering Leader specializing in SaaS, FinTech and HealthTech

Orange County, CA30y exp

“Engineering leader (Octane) who drove business-aligned technology roadmaps and scaled a fully distributed engineering org from 35 to 150 in 9 months using a rigorous ownership model (OARP). Led a greenfield architecture for a new vertical (AWS Lambda/ECS, Python/JS/React) that delivered an MVP in 3 months while cutting per-service cloud OPEX from ~$300+ to ~$20/month, with 15+ years of remote-first leadership experience.”

Gregory Walton

Screened

Senior Software Engineer specializing in SRE, cloud automation, and reliability tooling

Sunnyvale, CA11y exp

“Technically oriented builder focused on taking ambiguous LLM/agentic workflow needs from scoped problem definition to iterative prototypes, with emphasis on edge cases and customer feedback. Has hands-on experience debugging RAG/agent systems, including resolving a real integration issue involving search query quoting differences between manual search and an API, and is comfortable fielding deep developer Q&A in demos.”

“Product/operations-focused builder exploring an AI-based vacation planning app (early-stage). Previously drove a large-scale fulfillment-center automation initiative impacting 1.2M associates, reducing peak-season ad-hoc hiring and delivering ~$39MM in annual savings through a metrics-driven, cross-functional execution approach.”