Vetted SQL Professionals

“Strategic enterprise Customer Success leader from Atlassian Cloud managing a >$10M ARR, ~33k-user account end-to-end, driving measurable adoption (+12%), services expansion (+30%), and strong satisfaction (4.5/5). Experienced leading cross-functional deployments of AI agents (Rovo) and Forge-based integrations, and translating enterprise governance needs (e.g., RBAC at scale) into roadmap-shaping product requirements.”

“Robotics software engineer with hands-on ROS2 experience building an audio conversion node and integrating Whisper LiveKit for streaming speech-to-text in a simulated hostile (outer space) robot environment. Also worked on a 2023 LiDAR + ML vision obstacle-detection project for a hospital-nurse-assistant robot, and has strong large-scale CI/CD deployment experience from AWS (2022–2024) across alpha/pre-prod/prod stages.”

“LLM/RAG practitioner who built an AWS-based enterprise document search and summarization platform with RBAC and scaled it to 10K+ users, solving relevance issues via contextual chunking and hybrid retrieval. Also designed agentic workflows for a telecom forecast-validation use case using sub-agents, tool APIs, and strict context management, and has proven pre-sales influence (supported a $300K manufacturing deal with a roadmap-driven pitch).”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

“Built and deployed a healthcare NLP application that used an LLM-style physician interface feeding a random forest model to predict treatment plans for hard-to-triage patient subgroups, backed by a Databricks medallion pipeline and heavy feature engineering to address missing/low-integrity data across ~50K patients. Also delivered an earlier Microsoft AI Builder automation that improved transportation bill payment workflows by training non-technical payroll/procurement teams to use automated outstanding-payables reporting.”

“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”

“Embodied AI/robotics ML engineer with hands-on experience deploying POMDP-based reinforcement learning controllers on real mobile robots and vehicle fleets. Strong in sim-to-real robustness (domain randomization) and production rollout practices (HIL, shadow-mode, canaries, safety instrumentation), and has published related work (mentions a NeurIPS paper).”

“TikTok engineer (4 years) who built and owned multiple ad transparency platforms used by internal teams and advertisers worldwide. Strong full-stack profile spanning Next.js/TypeScript + Redux frontends, a secure/optimized BFF layer, and event-driven backend workflows (Kafka, retries/DLQ, Redis idempotency) with heavy emphasis on observability and performance; cites a 20% moderation efficiency gain on a modernized legacy tool.”

“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.”

“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.”

“Graphics software engineer with academic robotics/HRI experience at Oregon State University under Dr. Heather Knight, leading a ROS+Python physical robot and Unity/C# VR system to study how motion/texture/collisions are perceived in VR (2 papers + thesis). Also built ROS-based Wizard-of-Oz TurtleBot study systems and multi-robot coordination experiments, plus industry experience with Docker/Kubeflow ML tooling and Azure DevOps CI/CD automation.”

“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”

“Robotics engineer with UC Berkeley ROAR autonomous racing experience focused on real-time mapping/localization: implemented DLIO in ROS 2 and built the supporting LiDAR/IMU/GPS synchronization, TF consistency, and GPS-aligned trajectory tooling needed for reliable 3D SLAM on a physical vehicle. Also independently integrated a heterogeneous quadruped robot system at Eli Lilly spanning embedded, PLC, safety radar, Raspberry Pi, and cloud voice interfaces.”

“Backend/security-focused engineer supporting a service with 100k+ monthly users. Built an automated load-testing suite that reproduced and mitigated catastrophic host failures from oversized SCP/rsync transfers via host-level throttling, and proposed a future sharding approach for very large transfers. Also created an internal agent to summarize anomalous metrics and provide ready-to-run debug queries, significantly reducing ops review time.”

“Software/data engineer with experience at Google and on Bloomberg-related financial data modernization, building Python pipelines that convert legacy financial datasets into modern structures and iterating based on client feedback (e.g., adding historical change tracking for private placement data). Also built an internal Google usage-metrics dashboard pipeline using Protocol Buffers and scaled execution via sharded parallel cron jobs while scheduling off-hours to avoid impacting a testing tool.”

“Aspiring AI product builder interested in LLMs and deep learning, exploring forming a team (including fresh graduates) and leveraging crowdsourcing to develop ideas. Has not raised capital and has no VC/accelerator experience yet, but is thinking ahead about funding needs and partnering with an operational co-founder while potentially joining an existing team.”

“End-to-end product engineer who built and shipped Shopify Magic, an LLM-powered product-description generator on Amazon Bedrock with RAG over a tenant-isolated vector database, achieving 50% faster content creation, sub-2s latency, and 70%+ merchant adoption. Also led a Flexport migration from a monolithic Rails app to microservices using feature flags and parallel runs, delivering zero downtime and a 60% improvement in development speed.”

“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).”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”

“Growth and brand marketing leader with senior experience across gaming (PlayStation Plus; Head of Marketing at Devsisters; also mentions Imangi), known for data-driven UA scaling (LTV/ROAS), systematic creative testing, and integrated multi-channel launches that drove app store #1 rankings and improved retention/revenue. Also an active DJ and event producer with hands-on creator/influencer program experience, bringing strong creator-culture intuition to performance marketing.”

“Data-driven operator/analytics leader with experience spanning post-acquisition integration (Papaya Global/Azimo), executive BI systems (warehouse + dbt + Looker), and high-tempo operations leadership (managed Rappi’s Argentina fleet during COVID). Has repeatedly aligned senior stakeholders by defining KPIs upfront, building scenario models in SQL/Python, and operationalizing a trusted single source of truth for leadership.”

“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”

“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”