Vetted Docker Professionals

“Full-stack engineer who has owned customer-facing analytics and dashboard products end-to-end using TypeScript/React with Spring Boot microservices. Strong in scaling and stabilizing distributed systems (RabbitMQ, DLQs/retries, observability with correlation IDs) and in building internal tooling that consolidates ELK/CloudWatch signals to speed up support and operations; reported ~30% performance improvement on a recent dashboard.”

“Robotics/ROS2-focused early-career engineer who built a stereo visual-odometry SLAM system for autonomous navigation and optimized it to run reliably in real time on Raspberry Pi. Strong in sensor fusion (camera+IMU), ROS2 debugging/profiling, and distributed robotics/IoT pipelines (ROS2 + MQTT + cloud), with added experience extracting WiFi CSI for sensing/localization and shipping via Docker + GitHub Actions CI/CD.”

“Built a web-based online Sudoku game in JavaScript (multiplayer format supporting up to 6 teams with up to 5 players each) and demonstrates strong product/analytics orientation. Uses a KPI-driven approach (DAU/WAU, ARPU, session duration, LTV) and structured prioritization methods (MoSCoW, story mapping, cost of delay, DFV) to iterate toward targets; seeking a remote role around $70k/year.”

“Backend-focused engineer working primarily with Python/Django who also handles full-stack responsibilities. Has hands-on experience deploying containerized Python/Java microservices to Kubernetes using Helm and GitOps (ArgoCD), plus building Kafka-based event streaming with reliability controls (acks, consumer groups, DLQ). Also supported major on-prem to cloud/hybrid migrations using Terraform/Ansible with blue-green cutovers and data replication to minimize downtime.”

“Technical/presales engineer with experience at Grubhub and Appen, spanning LLM-adjacent data labeling workflows and production AI troubleshooting. Built an integrations platform at Grubhub and has hands-on experience diagnosing prompt-related AI failures via Splunk, adding JUnit tests and logging to prevent recurrence. Known for shipping customer-specific workflow adaptations (e.g., OCR annotation coordinate transformations for crop/rotation) while keeping timelines intact through iterative delivery and parallelization.”

“Backend engineer who has built high-throughput analytics and fraud-detection systems, combining Python/Flask + Celery/RabbitMQ with strong PostgreSQL performance tuning (indexing, partitioning, EXPLAIN ANALYZE). Has production experience integrating ML inference (scikit-learn/TensorFlow → TensorFlow Lite) into Spring Boot microservices with caching and model versioning, plus designing secure multi-tenant architectures using JWT-based tenant routing and PostgreSQL RBAC/RLS.”

“Master’s-in-Data-Science candidate (UHV) with 4+ years in AI engineering building production LLM and multimodal systems. Designed an LLM-powered workflow automation platform using RAG over vector stores with guardrails (schema/output validation, fallbacks) and a rigorous evaluation/monitoring framework including drift tracking and shadow deployments. Experienced orchestrating large-scale vision-language pipelines with Airflow and Kubernetes (OCR, distributed training) and partnering with non-technical ops stakeholders to cut cycle time and reduce errors.”

“Backend/platform engineer who owned a real-time customer analytics microservice stack in Python/FastAPI with Kafka streaming into PostgreSQL, including schema enforcement (Avro) and high-throughput optimizations. Strong Kubernetes + GitOps practitioner (EKS/GKE, Helm, Argo CD) who has handled CI/CD reliability issues with automated pre-deploy checks and rollbacks, and supported major migrations (on-prem to AWS; VM to EKS) with blue-green cutover planning.”

“ML/MLOps practitioner who built production systems for telecom network analytics, including an automated labeling + multi-label Random Forest solution that cut labeling effort by 90% and sped up RCA. Led an Ericsson auto-deployment platform using Airflow, Azure IoT Hub, Docker, and Celery to orchestrate 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment.”

“Backend engineer with hands-on manufacturing/production-systems experience at Wallbox, improving the Supernova charger rework process by streamlining part-number/component updates. Strong in building modular Python/Flask services with clean integration layers (Cosmos DB, NetSuite, traceability/label printing), plus deep SQLAlchemy/Postgres performance tuning. Also brings scalable AI/ML integration and deployment experience (OpenAI/Hugging Face/TensorFlow Serving, Docker/FastAPI/Nginx) and multi-tenant schema isolation with RBAC.”

“Backend engineer with Citi Bank experience building and operating a Python/Flask Personal Finance Manager platform at 1M+ transactions/month. Strong in secure API design, database performance tuning (PostgreSQL/Azure SQL), and production reliability (92%+ test coverage, load testing, monitoring). Also integrated an NLP expense-tagging microservice with caching, background workers, autoscaling, and multi-tenant isolation via RLS and tenant-aware JWT.”

“Designed and deployed an enterprise LLM-powered clinical/pharmacy policy knowledge assistant at CVS Health, replacing manual searches across PDFs/Word/SharePoint with a HIPAA-compliant RAG system. Built end-to-end ingestion and orchestration (Airflow + Azure ML/Data Lake + vector index) with PHI masking, versioned re-embedding, and production monitoring (Prometheus/Grafana), and partnered closely with clinicians/compliance to ensure policy-grounded, auditable answers.”

“Software engineer who built a reusable React component package (UI modules, auth helpers, API client wrappers) for an AI SaaS background-removal project, emphasizing performance (tree shaking/dynamic imports) and reliability (Jest + Storybook). Also delivered a unified REST API for Samsung Big Data Portal, resolving cross-team issues by standardizing schemas, improving validation/logging, and operating effectively amid shifting requirements.”

“Robotics software engineer who worked on NYU’s Medi Assist robot, owning navigation sensor bring-up (LiDAR/radar/IMU) and SLAM stability, plus delivering a safety-critical braking system. Built a YOLOv8 perception pipeline on Jetson Nano and wrote STM32 firmware to actuate brakes, achieving ~50ms reaction time, and implemented diagnostics/health checks and reliable inter-board comms (ROS2 + UART with checksums/heartbeats).”

“Built and deployed a production LLM-powered clinical note summarization system used by care managers to speed review of 5–20 page unstructured medical records. Implemented safety-focused validation (prompt constraints, rule-based and section-level checks, human-in-the-loop) to reduce hallucinations while maintaining low latency and meeting privacy/regulatory constraints, integrating via APIs into existing clinical tools.”

“Backend/ML integration engineer with experience at Accenture and Walmart building Flask-based analytics and prediction APIs on PostgreSQL/MySQL. Strong focus on performance and scalability—uses precomputed aggregates, Redis caching, query tuning (indexes/partitioning/EXPLAIN), and async/background processing; also designs secure multi-tenant isolation with JWT and schema/db-per-tenant strategies.”

“Robotics software engineer focused on ROS2 autonomy, with hands-on work building a monocular visual odometry system on KITTI (including GPS-based scale correction and RViz trajectory visualization) and an end-to-end Gazebo simulation integrating URDF, slam_toolbox, and Nav2. Demonstrates strong practical debugging skills around TF frames, lifecycle nodes, and Gazebo plugin/version compatibility.”

“Robotics software engineer who led development of a vision-based end-effector stability/vibration analysis tool using phase-based motion magnification and frequency-domain analysis (FFT/Bode) to uncover resonances missed by motor-only diagnostics. Experienced with ROS 2 C++ perception/navigation (ArUco + PnP) and real-time industrial integration, including optimizing a 1 kHz EtherCAT/Beckhoff PLC/Modbus TCP diagnostic pipeline and designing deterministic interfaces across heterogeneous subsystems.”

“Python backend engineer focused on real-time e-commerce analytics systems, building FastAPI + Kafka microservices with strong idempotent processing patterns (Postgres upserts, manual offsets, Redis caching). Has hands-on Kubernetes (EKS) and GitOps delivery with Argo CD/GitHub Actions, plus experience migrating containerized services from on-prem VMs to AWS using Terraform and blue-green cutovers.”

“AI/LLM engineer with production experience building secure, scalable compliance-focused generative AI systems (GPT-3/4, BERT) including RAG over internal regulatory document bases. Has delivered end-to-end pipelines on AWS with PySpark/Airflow/Kubernetes/FastAPI, emphasizing privacy controls, monitoring, and iterative evaluation (A/B testing). Also partnered closely with bank compliance officers using prototypes to refine NLP summarization/classification and reduce document review time.”

“Frontend engineer with Walmart Labs experience who contributed to the React Query (TanStack) open-source ecosystem by reproducing and helping fix a tricky cache invalidation edge case via a detailed GitHub issue and merged PR. Led measurable runtime performance improvements on a large B2B dashboard (D3.js), using memoization, component refactors, web workers, and virtualization to cut render time ~60% and eliminate UI freezes, and standardized data-fetching patterns to reduce data-related bugs ~30%.”

“Space/heliophysics ML engineer who built a PyTorch GRU model to propagate solar wind from L1 to the magnetopause with probabilistic outputs for uncertainty quantification, achieving ~25% better CRPS than standard approaches. Also developed production-grade Python ETL and an open-source telemetry processing package for a mission (LEXI), using Docker and GitHub Actions CI/CD and iterating with scientist/engineer stakeholders.”

“Full-stack developer who built an end-to-end Hotel Management System using React and Spring Boot with MongoDB and AWS. Has hands-on experience debugging API/data-fetching issues with Postman and validating results against the database, plus exposure to handling large data workloads with chunking and monitoring via Grafana/Tabula.”

“AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.”