Vetted Python Professionals

“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.”

“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.”

“Backend/data engineer with strong AWS production experience spanning high-traffic FastAPI APIs (Postgres/Redis/Kafka) and serverless+container deployments (Lambda/ECS) managed via Terraform and CI/CD. Has built Glue-based data lake ETL (S3 Parquet, Athena/Redshift) with schema drift/data quality controls, modernized legacy batch systems via parallel-run parity validation, and demonstrated measurable SQL performance wins (60–90s down to 3–5s).”

“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.”

“Senior engineering leader with experience at Cisco, Amazon, and startup Shopkick, operating at high scale (e.g., Secure Web Gateway handling ~40M QPS). Known for measurable impact across reliability and cost (85% efficacy improvement; Datadog spend cut from ~$500k/month to ~$15k/month) and for leading complex platform modernization (1-year monolith-to-microservices/event-driven migration with zero customer impact) plus compatibility-focused API design that cut device onboarding from a month to a day.”

“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.”

“LLM-focused engineer/sales-engineering profile with hands-on experience productionizing complex systems: scalable distributed architecture, multi-tenant monitoring, canary/shadow rollouts, and robust fallback strategies. Demonstrated real-time troubleshooting depth (p99 latency spikes traced to DB connection limits causing retry storms) and strong developer-facing communication via RAG workshops and live, customer-specific demos that helped close deals quickly.”

“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).”

“Robotics software engineer with extensive ROS2 academic project experience (UMDCP), including a drone-based 3D object reconstruction system using Mast3r where they built ROS2 nodes for autonomous image capture, containerized the ROS2/OpenCV stack for hardware deployment, and automated AWS uploads/compute-triggered reconstruction. Demonstrated strong sim-to-real debugging using ROS bags and PlotJuggler to correct yaw/trajectory offsets, and built multi-node TurtleBot navigation using visual cues (horizon/stop signal/obstacle detection) feeding a cmd_vel controller.”

“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).”

“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.”

“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.”

“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.”

“Backend/platform engineer with deep AWS experience who built a high-reliability ingestion platform (Lambda + Step Functions + DynamoDB) that became the single source of truth for training/qualification certification data across an AU region, handling high-volume async updates with strong consistency controls. Also led a major API migration from Lambda to ECS/Fargate to eliminate cold starts and increase throughput, and has hands-on EKS/Kubernetes operations plus Kafka partitioning/ordering expertise.”

“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.”