Vetted Docker Professionals

“Google Health engineer who owned and shipped an AI-powered clinical insights dashboard and NLP clinical note extraction service end-to-end (React/Next.js frontend; Python/Node microservices on GKE; TensorFlow transformers; BigQuery analytics). Demonstrated strong production rigor (CI/CD, testing, observability, guardrails for sensitive data) and delivered measurable outcomes including 30% faster diagnostics, 40% less manual documentation, 15% higher adoption, and 25% lower ops costs.”

“LLM/agentic systems practitioner who partners directly with customers to productionize prototypes end-to-end—defining business-aligned metrics, building evaluation datasets, and shipping monitored, cost-bounded inference APIs on AWS Lambda. Notably delivered a vehicle damage classification system that cut manual review by 40% and stabilized agent workflows by instrumenting state transitions to uncover and fix a race-condition-driven skipped tool call.”

“Worked on an AWS DynamoDB Journal team project building internal operator dashboards end-to-end, creating Java/Spring Boot APIs and integrating them into a Spring Boot/Thymeleaf/JavaScript UI to speed up debugging workflows. Has experience with data-heavy web apps and performance techniques (load balancing, caching, pagination, compression) plus hands-on debugging across unit/integration/E2E tests; also maintained and enhanced a React website at Global Spark.”

“Software-focused candidate who built a data monitoring pipeline during a hedge fund internship, integrating real databases and an email API to notify teams when data was ready. Comfortable working through legacy/scrappy code and uses LLMs to accelerate comprehension and delivery, with an emphasis on thorough testing and clear communication with stakeholders/customers.”

“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”

“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”

“Backend/data engineer with production experience at Uber building a near real-time driver rewards service on AWS (FastAPI, PostgreSQL, Redis) with strong reliability and concurrency controls. Also delivered AWS Lambda/ECS containerized deployments with GitHub Actions CI/CD and cost governance, built AWS Glue ETL with schema-evolution handling, and drove a ~10x SQL performance improvement while owning incident response via CloudWatch.”

“Masters capstone lead for a search-and-rescue rover that maps earthquake-affected environments using ROS2, SLAM (Cartographer), Nav2, and computer vision (OpenCV/ArUco). Served as both project manager and systems/architecture integrator, debugging IMU-driven navigation issues via RViz and implementing an A*-based planning workaround; also designed a supervisor/robot-side architecture to scale toward multi-robot coordination.”

“Software engineer focused on personalization and data/ML infrastructure who built a GenAI/LLM-driven carousel ranking system end-to-end, delivering a reported 6–7% order-rate lift. Also designed large-scale personalization ETL (15PB for ~100M users) and created a custom Airflow operator to integrate with Databricks under enterprise version constraints, with hands-on on-call and data-quality reliability improvements.”

“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”

“Backend engineer at Samsara with prior Amazon internship experience; built an internal Python chatbot for documentation discovery with a full security model (authn/authz and credential-based response filtering) in a sensitive environment. Currently works on dashcam/device-backend systems, including feature-flagged migrations for audio alert behavior and resilient real-time gRPC streaming under unreliable cellular connectivity, while actively monitoring Buildkite/Toolshed CI/CD rollouts.”

“Player-coach engineer/leader with experience at Twist Bioscience and Series B startup Vida Health, owning high-impact platform work across frontend and backend. Delivered a large-scale permissions migration to Okta affecting ~100,000 users and led reliability/process improvements (weekly releases, dedicated QA/dev environments, incident response with RCA) in high-growth environments.”

“Backend/cloud engineer with deep Azure and distributed-systems experience: owned an end-to-end Python multi-orchestrator config generator (Contrail) spanning OpenStack/vCenter/OpenShift/Kubernetes via a translation-layer approach. Built GitOps-style ARM-template infrastructure and CI/CD with automated testing, including a zero-downtime Databricks-to-Synapse migration using parallel production validation. Worked on Microsoft Azure Identity gateway (reverse proxy for auth) and executed ring-based deployments for major platform migration.”