Vetted Observability Professionals

“Full-stack engineer with deep AWS serverless and reliability experience in fintech/underwriting systems, including eligibility scoring and dynamic rule deployments. Built and productionized an LLM-powered incident RCA assistant (Bedrock Claude 3 + custom RAG + React) achieving 92% precision and ~75% MTTR reduction, with mature guardrails (evals, drift monitoring, HITL, audit logs) and strong operational rigor (canaries, chaos testing, DLQ remediation).”

“Full-stack/backend engineer who owned end-to-end delivery for DentalScan, an AI-powered healthcare platform, building Kafka-based Node.js microservices plus Next.js clinician dashboards and CI/CD on Vercel. Productionized LLM/voice AI patient communication (Twilio/Retell AI) with prompt templates, guardrails, and monitoring, supporting 1,000+ scans/day at sub-100ms latency and reducing clinic admin workload. Previously modernized an agricultural data platform at Infosys to Spring Boot/Docker microservices with Redis, Jenkins CI/CD, and AWS CloudWatch monitoring.”

“Engineering leader who delivered a Magento-to-Shopify rebuild ahead of schedule while maintaining 100% team retention over 4 years at Hydrobuilder Holdings. Also built and ran a concert-listing platform (TourVolume) in production for 12+ years (Webby Honorable Mention) and later open-sourced the full codebase. Deep experience in auth/API platforms on AWS, including moving critical auth services from Lambda to ECS to eliminate cold-start latency.”

“Customer-facing platform support professional focused on application security and reliability for SaaS integrations, with hands-on experience troubleshooting API auth failures and data update issues using Linux/app/DB logs, SQL, and monitoring. Demonstrates strong security fundamentals (least privilege, TLS/access controls, credential rotation) and can design secure AWS agent integrations while also supporting Azure containerized REST API deployments via CI/CD.”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”

“Customer-facing application security/support professional who helps teams operationalize SAST/DAST/SCA in CI/CD, reduce false positives, and prioritize remediation by business risk. Has hands-on experience troubleshooting intermittent pipeline failures (rate limiting/timeouts under parallel load) using logs/metrics/traces, and driving durable outcomes via runbooks/KB documentation and phased security rollouts across stakeholders.”