Vetted Python Professionals

“Backend/AI engineer who built and scaled an internal AMD semiconductor manufacturing microservice platform (SMR), reworking a synchronous lot-request workflow into an event-driven RabbitMQ/Celery/FastAPI pipeline. Diagnosed and fixed peak-load reliability issues using deep observability and Kubernetes autoscaling, cutting notification latency back to sub-second and reducing duplicates via idempotency/DLQs. Also shipped an LLM-powered natural-language search with schema-constrained JSON outputs and guardrails, plus a plan-execute-verify Jira bug-resolution agent that can propose fixes and raise PRs under restricted permissions.”

“Full-stack engineer (~3 years) with eBay production experience building and operating high-scale, event-driven Python microservices for order processing and AI-powered recommendations (Kafka/Redis/FastAPI on AWS with Prometheus/Grafana). Also delivered polished React+TypeScript analytics dashboards and designed high-concurrency PostgreSQL schemas with significant latency reductions. Recently built AI-agent orchestration and an interactive node-based requirements dashboard for Siemens Polarion via MCP servers, improving user interaction by ~17.8%+.”

“Backend/product engineer with Tesla experience building and operating a real-time OTA update monitoring and fleet analytics platform at massive scale (telemetry from 3M+ vehicles). Delivered end-to-end systems across Kafka-based ingestion, TimescaleDB/Postgres analytics modeling, FastAPI/GraphQL APIs, and React/TypeScript dashboards, and handled production scaling incidents on AWS EKS during major rollout spikes.”

“Full-stack engineer with production experience at Atlassian and Zoho, spanning GraphQL federation, React/TypeScript frontends, and cloud-native AWS/Kubernetes operations. Built and operated a federated GraphQL gateway with Terraform + CI/CD + observability, delivering major latency and integration-time improvements, and also designed high-volume Kafka data pipelines (10M+ events/day) with strong reliability guarantees.”

“Summer intern on AWS Lambda tooling team who shipped Finch support in AWS SAM CLI, adding OS/runtime detection and robust fallback behavior to preserve Docker compatibility across developer environments. Also built an end-to-end RAG system for querying arXiv quantitative finance papers using Postgres/pgvector with two-stage retrieval, citation-grounded prompting, and rigorous evaluation loops driven by IR metrics and user feedback.”

“Owned and built Bloomberg’s end-to-end bitemporal dividend & dividend-forecast data platform powering BQL for 400k+ terminal users. Architected real-time Kafka ingestion (5k–10k msgs/sec) across 100k+ tickers with strong correctness guarantees (PIT/bitemporal time-travel, immutable history to avoid look-ahead bias) and achieved sub-100ms p95 query latency through indexing and caching, deployed with Kubernetes + DLQ and robust monitoring.”

“Built a production LLM/RAG-based “model excellence scoring” system at Uber to automatically evaluate hundreds of ML models, standardizing quality assessment and cutting evaluation time from days to minutes on GCP. Also delivered an NLP document classification solution for insurance claims at Globe Life, partnering closely with compliance/operations and improving routing accuracy from ~85% manual to 93% with the model.”

“UE5 gameplay animation developer focused on building and optimizing full character Animation Blueprint stacks (locomotion + jump/fall/land, montages, linked anim graphs, and layered procedural IK). Has hands-on experience debugging tricky interaction IK issues (e.g., 180-degree turn misalignment) and improving runtime performance using Unreal profiling tools and conditionalizing IK/Control Rig evaluation.”

“Data engineer with experience at Moderna and Block owning high-volume (≈10TB/day) production pipelines on AWS, using Kafka/S3/Glue/dbt/Snowflake with strong data quality and observability practices (schema validation, anomaly detection, CloudWatch monitoring). Also built external financial API ingestion with Airflow retries, throttling/token rotation, and schema versioning, and helped stand up an early-stage biomedical data platform with CI/CD and incident debugging.”

“Data engineer (Intuit) who owned an end-to-end telemetry and subscription analytics platform processing ~22M events/day, built on Kinesis/S3/Glue/Spark/Airflow/Redshift. Strong focus on reliability and data quality (schema drift controls, quarantine layers, idempotent reruns) and performance tuning, achieving a reporting latency reduction from ~15 minutes to under 4 minutes while enabling revenue and churn analytics for business teams.”

“Autonomous driving perception/mapping engineer who designed a vectorized local mapping module end-to-end across labeling, training, and evaluation for multi-modal inputs (vision/LiDAR/nav maps). Implemented automated evaluation/regression testing and improved occlusion/long-range precision by ~5% using temporal features and transformer-based reference points, while optimizing models via pruning/EfficientNet to fit system resource constraints.”

“LLM/agentic workflow practitioner (Continuum AI) who productionized an LLM system for manufacturing RMA intake and warranty claims by moving from a brittle prompt to a modular pipeline with RAG, function-calling extraction, deterministic validation, and strong observability. Also diagnosed and fixed an agentic ticket-triage misrouting issue by tracing failures to retrieval timeouts, adding guardrails/fallbacks, and implementing retries plus continuous evaluation—bringing misroutes near zero while creating a repeatable debugging playbook.”

“Robotics-focused engineer who built a non-holonomic self-driving car on Raspberry Pi 5 using ROS 2, implementing sensor fusion (robot_localization EKF), 2D SLAM (slam_toolbox), custom Hybrid A*/RRT* planners, and MPC trajectory tracking. Demonstrated strong real-time debugging and performance tuning (timestamp sync, CPU contention mitigation) and is extending the platform toward CV-based plant identification and autonomous plant watering.”

“Robotics/ML engineer who implemented TD3 and PPO in PyTorch to solve the challenging OpenAI Gymnasium humanoid-v5 MuJoCo task, including custom networks, rollout logic, and training scripts. Also has hands-on robotics coursework experience with ROS-based RRT motion planning on a real robotic arm, plus practical CI/CD and containerization experience (Docker, Jenkins, GitHub Actions). Currently exploring world models (VAE + sequence generator) using Euro Truck Simulator data.”

“Master’s capstone contributor on an autonomous rover navigation project, serving as an embedded/robotics software designer. Built low-level wheel control and odometry from encoders, integrated RealSense and RPLidar via ROS, and solved sensor-fusion/coordinate-frame issues by creating custom TF transforms. Used Gazebo to debug sim-to-real behavior and improved reliability on rough terrain by moving to dual-channel encoders when IMU data proved unreliable.”

“Gameplay engineer with hands-on experience across Unreal Engine 5 and Unity, owning systems from level streaming/teleportation to motion-matched character movement. Has shipped/implemented multiplayer features including GAS-based replication with prediction/reconciliation and a deterministic lockstep runner using fixed-point math. Strong at diagnosing hard-to-repro physics issues and balancing performance/feel (e.g., mobile voxel A* pathfinding at 60 FPS, Chaos destruction tuned to avoid trapping players).”

“Co-founded and led the business side of Plasma Nutrition for a decade, commercializing atmospheric-plasma technology to improve protein efficacy/absorption. Bootstrapped for 5 years before raising venture funding, leveraged FoodX accelerator validation, and navigated a major go-to-market pivot (B2C to B2B). Drove growth by renegotiating out of an exclusive manufacturing arrangement and scaling to roughly six brand partners within a year.”

“Robotics software engineer at Tesla who led end-to-end development of a distributed real-time control and orchestration platform for autonomous systems. Deep production ROS 2 experience (nav2, slam_toolbox), with demonstrated wins reducing end-to-end latency 25–30%+ via profiling, multithreaded executors, and QoS tuning, plus simulation and deployment at scale using Gazebo/Webots, Docker/Kubernetes, and CI/CD.”

“Built the backend for “codeGuard,” an AI-powered static code analysis platform, using FastAPI and Docker. Structured the system into API/service/execution layers and addressed heavy-workload container resource/cleanup issues via strict CPU/memory limits and a queued execution model.”

“Internship experience building a production LLM-powered podcast operations agent that automated lead intake (HubSpot), guest research, scheduling (Calendly), meeting-summary evaluation (Gemini), and human approval via Slack bot—while retaining rejected candidates for future outreach. Also contributed to ideation of a multi-agent orchestration framework with parsing and task routing, and emphasized reliability via structured prompts, HITL feedback, and prompt-based test sets.”

“Robotics software engineer who built and owned an autonomous laparoscope tracking system on a UR3e with an eye-in-hand RealSense camera, integrating YOLO-based tool detection with velocity control under a strict RCM constraint and deploying successfully in a hospital setting. Deep ROS2/MoveIt2 experience (architecture, QoS, custom nodes) plus autonomy stack work across SLAM, planning, and real-time latency/control debugging.”

“LLM engineer who has deployed privacy-preserving, real-time workplace risk monitoring over massive enterprise chat/email streams, tackling latency, hallucinations, and extreme class imbalance with model benchmarking, RAG + fine-tuning, and a pre-filter alerting layer. Also built an agentic legal contract drafting system (Jurisagent) using LangGraph/LangChain with deterministic multi-agent control flow, structured outputs, and reliability-focused evaluation/telemetry.”

“Built a production LLM-powered analyst assistant at Discern Security to speed up SOC investigations using a RAG pipeline over security vendor documentation (Python PDF ingestion, vector search). Demonstrates deep, security-critical LLM engineering: structure-aware chunking with custom table parsing, grounded/cited responses, prompt-injection defenses, and post-generation validation, validated via golden datasets and adversarial testing; tool is used daily by analysts.”