Vetted Observability Professionals

“Robotics engineer from UIUC’s Intelligent Motion Lab who led the perception stack for a humanoid robotic nurse, fusing camera/LiDAR/IMU on NVIDIA Jetson Orin for real-time localization and scene understanding across six robots. Deep expertise in ROS 2 and edge ML optimization (TensorRT, CUDA, zero-copy), delivering major latency/throughput gains (10 FPS to 22+ FPS) and building fault-tolerant pipelines with gRPC offloading and real-time reliability practices.”

“Backend/data engineer with strong authorization and compliance-domain experience: led a phased migration from a simplistic role model to modern RBAC on a Python serverless stack (Auth0 + AWS Lambda/API Gateway), coordinating changes across 5 repos with extensive manual and automated validation. Previously built and operated custom ETL pipelines (Airflow + Groovy/Java on Spark/YARN/Hadoop) to normalize messy customer email/chat/voice data for NLP-driven financial compliance indicators, including complex email journaling metadata enrichment and large-scale remediation reprocessing after production bugs.”

“Senior engineering/CTO-level leader with hands-on delivery of serverless, event-driven cloud governance platforms (deployed across multiple GE business units) and experience building adaptable usage-based billing at UserTesting. Has advised startups (including CaseText during YC) and supported fundraising and acquisition due diligence, including investor materials for a smart cooler custody management system presented to BARDA during Operation Warp Speed.”

“Full-stack engineer who has owned an operational reporting/dashboard product end-to-end—building a React UI, designing/implementing FastAPI services, and deploying/operating on AWS. Demonstrates strong performance engineering (Postgres query/index tuning using EXPLAIN ANALYZE) with concrete impact (reports reduced from tens of seconds to a few seconds) and a reliability mindset across observability, migrations, and resilient third-party/ETL integrations.”

“Has hands-on familiarity with successful F2P mobile fighting games (e.g., Marvel Contest of Champions) and can articulate key retention/monetization systems like streak-based daily logins, gacha rewards, and limited-time events. While not having shipped a game directly, they have shipped web/mobile products integrated into gaming ecosystems and think in terms of live-ops health metrics and A/B testing for IAP offers.”

“Former Walmart Director of Software Engineering who left in March 2025 to build products for clients. Recently delivered an LLM/RAG-based UNSPSC classification solution for an MRO client using a multi-stage retrieval + web search + prompt-engineering workflow, and has led large-scale retail forecasting initiatives and high-severity cloud-migration incidents end-to-end.”

“Engineering Manager/Tech Lead on Grindr’s monetization team who helped ship an AI-powered conversation summary feature (A-list), contributing across Android freemium implementation and backend LLM workflow service architecture/reviews. Demonstrated strong operational ownership by leading a Boost production incident from detection through rollback and prevention, and improved team throughput by introducing a lightweight end-to-end delivery process in a high-growth environment.”

“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”

“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”

“Backend/data engineer with production experience building FastAPI services with strong reliability patterns (circuit breaker, rate limiting, caching, graceful degradation) and JWT/OAuth2 auth. Has delivered AWS EKS deployments via Terraform with Secrets Manager/IRSA and HPA autoscaling, and built Glue/Spark ETL pipelines on S3 Parquet with schema-evolution and idempotent reruns; also demonstrated measurable SQL tuning impact (20–30s to <10s).”

“Backend/distributed-systems engineer with Uber experience building real-time telemetry and safety signal pipelines. Strong in Kafka-based event-driven architectures, low-latency processing under peak load, and production reliability via monitoring, retries, and fallback logic; has Docker/Kubernetes and CI/CD deployment experience.”

“Software engineer with experience at Amazon and Agora building end-to-end systems: a knowledge-base AI chatbot (React/TypeScript UI + retrieval/response backend + Docker deployment) and an internal approval governance platform using AWS Step Functions and DynamoDB. Emphasizes fast iteration without sacrificing trust via feature-flag rollouts, citation-required answers, abstention on low-confidence retrieval, regression query sets, and strong observability (request IDs, structured logs, latency/error monitoring).”

“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”

“Safety-critical firmware validation engineer with Tesla autonomous vehicle experience who built Python-based HIL/SIL automation and dashboards, cutting regression time by 30% while maintaining an auditable risk-tradeoff process with safety and engineering teams. Also deployed an inventory management system across 8+ R&D teams in 3 countries at FUJIFILM, troubleshooting a major cross-site sync issue to a timezone root cause with strong documentation and interim mitigations.”

“Full-stack engineer with deep serverless AWS experience who built VidToNote, an AI video analysis platform, end-to-end using Next.js App Router/TypeScript and an event-driven pipeline (API Gateway, Lambda, DynamoDB, S3, Step Functions, SQS). Strong on production reliability and observability (CloudWatch, X-Ray, structured logging), plus data/analytics work in Postgres with measurable query optimizations and durable LLM evaluation workflows. Amazon background; integrated 22 AWS services and completed AWS Solutions Architect Professional certification within a month.”

“Full-stack engineer in the investment tracking/financial reporting space who built an automated reporting dashboard and compliance/reporting pipeline end-to-end using Next.js (App Router, server/client components), REST, and Postgres. Demonstrated measurable performance wins (~30% faster loads) through caching and query optimization, and built durable orchestrated workflows in n8n with retries, idempotency, and reconciliation checks.”

“Cofounder at MeowyAI who shipped a production multimodal (vision/voice/text) AI task manager using Gemini, tackling real-world issues like hallucinations, tool-calling safety, and RAG-based preference memory. Also built a production multi-agent RAG system orchestrated with LangGraph (and contributes to LangChain), with strong emphasis on latency optimization, observability (OpenTelemetry), and rigorous testing/evaluation including A/B tests and adversarial prompting.”

“Internship experience at Intuit building an LLM-grounded QA system for internal microservice data across 100+ microservices, using a graph database approach (evaluated Neo4j and selected AWS Neptune for production alignment). Also has UC Berkeley research experience (including work with Prof. Dawn Song / Berkeley Eye Research Lab) and cross-functional collaboration with bioinformatics/biology teams to deploy software systems on research servers.”

“AI/LLM practitioner with internships at Dell Technologies and Roche who built and deployed a healthcare-focused "Doctor LLM" by fine-tuning Meta Llama 3.2 on healthcaremagic.json, emphasizing safety guardrails to prevent harmful medical advice. Experienced in productionizing AI workflows with monitoring, testing, and orchestration (Airflow, Kubernetes), and in delivering AI-agent-driven competitive landscape insights to non-technical business stakeholders.”

“Built and deployed a production LLM-powered risk assistant for KPMG and Freddie Mac that lets analysts query a confidential Neo4j risk graph in natural language (no Cypher), turning multi-day analysis into minutes with traceable, cited answers. Implemented rigorous guardrails, deterministic verification, RBAC/security controls, and a full eval/observability stack, cutting query error rate by ~50% and iterating through weekly UAT with non-technical risk analysts.”

“Backend owner at Apple for an AI workflow orchestration service, with hands-on experience stabilizing peak-traffic production systems using OpenTelemetry-style tracing, bounded async concurrency, and database performance tuning. Built and shipped a Python LLM-agent orchestration layer to automate multi-step operational workflows, emphasizing guardrails, auditability, and deterministic fallbacks to keep non-deterministic AI behavior production-safe.”

“Data/ML engineer who has owned end-to-end systems across email deliverability/segmentation and production LLM apps. Built a Spark+Airflow segmentation engine that materially improved deliverability (99.9%) and open rates (>50%), and shipped a PDF-to-quiz RAG product using LangChain/Vertex AI/Chroma with strong guardrails and an eval loop that cut hallucinations to <5%.”

“Platform and distributed-systems leader (player-coach) who owned architecture and reliability for an Amazon analytics/data platform serving ~100K internal users at exabyte scale. Built an ML-driven “Lakeflow” optimization layer that cut pipeline completion times ~20–25% and reduced compute waste >15%, and led major incident response/redesign efforts (e.g., deletion storm) with strong rollout/observability/rollback practices.”