Vetted Docker Professionals

“Infrastructure reliability/capacity-focused engineer with hands-on IBM Power/AIX (LPAR/DLPAR, HMC, VIOS) performance troubleshooting and modern cloud-native delivery experience. Built production CI/CD and Terraform-managed AWS/EKS environments, and has led real incident recoveries spanning Kubernetes autoscaling and AWS quota constraints with concrete RCA and prevention improvements.”

“Backend-leaning full-stack engineer with Walmart experience building and operating high-volume media upload and processing systems. Strong in Java/Spring Boot, Postgres performance tuning (EXPLAIN/ANALYZE), and durable workflows using Kafka/Spring Batch with retries and idempotency, plus production ownership via monitoring and optimization; familiar with Next.js/TypeScript and modern React performance patterns.”

“Full-stack engineer with production ownership across React/TypeScript, Node/Express, and Postgres, including zero-downtime releases and rollbackable migrations. Demonstrated measurable performance wins (20% response-time reduction) through DB query profiling and batching, plus hands-on AWS operations (ECS/Lambda/CloudWatch) and reliability patterns for ETL (retries, DLQs, idempotency). Experience shipping microservices quickly in ambiguous, fast-paced environments (Deloitte).”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

“Built and deployed a production LLM-powered RAG document intelligence/Q&A system for healthcare prior authorization, reducing manual medical document review time and improving decision efficiency. Strong in end-to-end LLM application engineering (LangChain/LangGraph), retrieval quality improvements (hybrid search, embedding tuning, chunking strategies), and rigorous evaluation/monitoring for reliability.”

“Software engineer with experience at Molina Healthcare and Target, owning production features end-to-end across backend, data pipelines, and UI. Built an event-driven claims validation system (Python/Java/Spring Boot/Kafka) with strong observability, and shipped embeddings-based semantic product search with evaluation loops (CTR/top-k + human review) and guardrails like keyword-search fallback.”

“Player-coach engineering leader focused on buyer/distribution product lines, building scalable purchasing/planning frameworks and modernizing workflows. Drove performance and reliability improvements via queue-based async architectures, external API redundancy, and CI/CD automation, and has led production incident response (cache-related) with follow-up playbooks and monitoring. Experienced in high-growth/startup environments, combining hands-on delivery with mentoring, 1:1s, and performance coaching.”

“Full-stack engineer with e-commerce experience who shipped and owned an order history dashboard using Next.js App Router/TypeScript, combining server components for SEO/perf with client-side interactivity via React Query. Has backend reliability experience (Temporal order-processing workflows, Postgres modeling/indexing, and payment API idempotency keys), and emphasizes production stability, observability, and zero-incident launches.”

“Data engineer who has owned end-to-end production pipelines for customer transaction data (~2–5 GB/day) using Python/PySpark/SQL and Airflow, delivering major reliability and speed gains (70% faster reporting; 60–70% fewer data issues). Also built a daily external web-scraping system with anti-bot handling and safe, idempotent Airflow-driven backfills, plus a Python data API optimized with indexing/caching and tested for correctness.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“Full-stack/AI-focused builder who shipped a production Career Advisor app using LLMs + RAG + vector DB (React/Node/MongoDB/Claude API) and grew it to 2000+ users, handling real deployment issues and CI/CD on Vercel/Render. Also developing an AI-powered iOS “3D World Explorer” (text-to-3D) and has cloud experience across Azure and AWS (S3/SageMaker/EC2).”

“UPenn robotics researcher who architected a real-time autonomous driving decision-making engine, integrating LSTM trajectory prediction with MPC in CARLA and adding conformal prediction to deliver 95% statistical safety guarantees under strict latency constraints. Also built and debugged an autonomous quadrotor stack with ESKF-based 6-DoF tracking and optimized A*/Dijkstra planning to eliminate latency-induced instability, with experience bridging heterogeneous simulation/control systems.”

“UCLA RoMeLa research assistant (since Oct 2025) building an embedded control and sensor-data platform for multi-robot coordination in a simulated warehouse. Deep hands-on experience with ROS on NVIDIA Jetson under RTOS constraints, secure MQTT/TLS telemetry, and SLAM performance optimization (including ORB-SLAM3) validated in Gazebo and deployed via Docker/Kubernetes and CI/CD.”

“Robotics software engineer with hands-on GPU/CUDA vision work (solo-built a 4-fisheye panorama stitcher using camera intrinsics/extrinsics) and mapping/localization expertise, including radar-driven pose-graph mapping optimized with Ceres. Strong ROS background (Cartographer, AMCL, TEB) and demonstrated localization improvements by biasing AMCL with Cartographer to reduce drift; experience shipping modules deployed across large robot/vehicle fleets (e.g., retail scanning robots and automotive).”

“Robotics software engineer focused on autonomous eVTOL operations, including simulated autonomous ship deck landing using ROS2 Humble with perception (AprilTags) and motion planning under aircraft dynamics constraints. Has hands-on experience with multi-robot coordination, SLAM sensor-fusion fixes, and distributed robot networking (LTE + VPN), plus embedded data capture on Jetson AGX Orin and advanced control methods (MPC/CBF, differentiable learning).”

“Backend/data platform engineer who recently owned an end-to-end large-scale financial data platform delivering real-time decision support for finance and operations. Has hands-on experience modernizing legacy batch pipelines into AWS cloud-native ELT with parallel-run cutovers, strong data quality controls (dbt-style tests, reconciliation), and measurable improvements in runtime, cost, and SLA compliance. Also builds scalable, secure FastAPI microservices using Docker, ALB-based horizontal scaling, Redis caching, and managed auth with Cognito/Supabase plus Postgres RLS.”

“Autonomous robotics student who built an end-to-end ROS2 semantic goal navigation system as a solo course project, integrating CLIP-based vision-language understanding with SLAM Toolbox and Nav2 to execute natural-language commands in Gazebo/RViz. Also implemented and tuned an RRT planner from scratch in Python and uses Docker plus GitHub workflows for reproducible, tested robotics codebases.”

“Robotics/control-focused engineer who built and validated a series elastic actuator control stack end-to-end (dynamic modeling, torque/position control, simulation, and experimental real-time debugging on hardware). Deep simulation background (OpenFOAM/COMSOL/Abaqus) and practical reproducibility tooling (Docker/CI), with conceptual ROS/ROS2 knowledge and confidence ramping into ROS-based stacks.”

“Backend/platform engineer who built a high-scale audience segmentation and real-time targeting system using Spark/Glue + S3/Hudi and low-latency API services backed by Redis/relational stores. Demonstrates strong production rigor: Spark performance tuning to eliminate OOM failures, API idempotency/caching to cut p95 latency ~40%, and careful dual-run/feature-flag migrations with reconciliation and rollback runbooks. Experienced implementing layered security with JWT/OAuth, RBAC/ABAC, and database row-level security to prevent privilege escalation.”

“Full-stack engineer who built an LLM-powered productivity web app (LifeOS) end-to-end with TypeScript/Next.js, Prisma, and Postgres, emphasizing fast iteration with stable API contracts and an isolated AI service boundary. Also built a security/compliance login-verification workflow at Medpace used within an internal admin portal for thousands of employees, and has AWS experience orchestrating batch GPU workloads with robust retry/idempotency patterns.”

“Robotics software engineer with hands-on experience delivering autonomous pick-and-place: built a depth-camera perception pipeline for tiny (15–20mm) parts using YOLO+SAM segmentation feeding Open3D ICP/RANSAC pose estimation and validated it end-to-end with ABB YuMi/RobotStudio. Strong ROS 2 integrator (Nav2, SLAM Toolbox, MoveIt2, Behavior Trees) who has debugged real TurtleBot3 odometry/latency issues and redesigned system architecture to improve reliability.”

“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”

“Built and deployed a production LLM-driven Dungeons & Dragons game where the model acts as a dungeon master, adding a structured combat system and a macro-state tree to ensure campaigns converge to a clear ending. Fine-tuned Gemini 2.5 Flash on Vertex AI and deployed on GCP with Kubernetes, using RAG over DnD rules/spells plus multi-agent orchestration (intent-based routing between narrative and combat agents) to reduce hallucinations and improve reliability.”