Vetted Kubernetes Professionals

Keano Zamora

Screened

Mid-level Software Engineer specializing in backend, platform, and cloud infrastructure

King of Prussia, PA4y exp

“Full-stack engineer with experience owning a customer-facing data platform at GDIT, built on Django and Next.js. Demonstrated pragmatic architecture choices to handle image-heavy deployments and helped evolve the frontend from Alpine.js to a consistent React-based stack.”

Shreya B

Screened

Mid-level Software Engineer specializing in backend, cloud-native, and GenAI systems

Redmond, WA5y exp

“Software engineer with strong Java/Spring Boot backend depth and hands-on full-stack experience building AI-powered enterprise knowledge assistants and customer-facing order tracking systems. Stands out for combining RAG/LLM product work, event-driven microservices, and user-trust-focused product iteration, including shipping prototypes that became the basis for broader production workflows.”

“Full-stack engineer who built and owned a production real-estate search platform (advanced search + saved-search alerts) using Next.js App Router/TypeScript with a NestJS + Postgres + Elasticsearch/Kafka backend. Demonstrated strong performance engineering (map search FPS ~20→60, ~80% latency reduction) and backend scalability (optimized alert-matching queries and orchestrated notification workflows with Airflow/Redis), with measurable post-launch engagement gains (+27% returning users).”

“Full-stack engineer with 5+ years of experience who built real-time data visualization and analytics systems at Uber, spanning React/TypeScript frontends, Node/GraphQL services, Kafka pipelines, and PostgreSQL. Particularly compelling for teams needing a hands-on builder who can turn ambiguous customer needs into scalable products, and who has also applied RAG with LangChain/OpenAI over 1.8M support files to surface actionable insights.”

“Software engineer with 5+ years of experience building and scaling high-traffic web systems, including e-commerce-style purchasing/reservation flows and broker back-office platforms. He has hands-on experience across Next.js front ends, Python integrations, Go load-testing tools, and MySQL/Redis-backed architectures, with notable exposure to financial reporting, MT5 integrations, and commission logic.”

Melbourne Brown

Screened

Senior Software Engineer specializing in AI-driven cloud-native platforms

Atlanta, GA12y exp

“Engineer with unusual breadth: from a tiny startup building racehorse medical-record systems on credit-card chips for live racetrack demos to modern AI-powered contract intelligence platforms in production. Brings hands-on full-stack and backend depth across React, Python, .NET, PostgreSQL, Kubernetes, and Azure, with a track record of making complex, reliability-sensitive systems work in real-world conditions.”

Sunil Chinthaparthi

Screened

Senior Software Engineer specializing in cloud-native backend and distributed systems

New York, NY4y exp

“Backend engineer focused on Python/FastAPI microservices running on Kubernetes (AWS EKS) with strong GitOps/CI/CD ownership (GitHub Actions + ArgoCD). Demonstrated measurable performance wins (p95 latency cut from >1s to <200ms) and production reliability work across Kafka/Redis streaming and cloud-to-on-prem migrations (RDS/S3 to Postgres/MinIO) using parallel validation and checksum-based consistency checks.”

“Senior engineering executive who has spent the last decade leading large-scale platform modernization while staying deeply hands-on in architecture, debugging, and code reviews. He has led 120+ person organizations, built cloud-native SaaS and identity platforms across regulated and enterprise domains, and ties engineering decisions directly to revenue outcomes, including contributing to Wind River's $1B+ milestone.”

“Co-founded a Digital Loss Prevention (DLP) startup and raised $6M in seed funding by showcasing a controlled, laptop-based technology demo. Post-funding, drove MVP planning and execution by sequencing operations and assembling a team to build an appliance MVP, using an iterative build/evaluate/visualize approach.”

“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 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.”

“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.”

“Software engineer who owned customer-facing, high-traffic TypeScript/React + TypeScript backend systems end-to-end, emphasizing safe velocity through feature flags, staged rollouts, observability, and rollback-ready incremental delivery. Reports shipping more frequently with fewer production incidents and faster recovery due to these guardrails.”

“Software engineer with experience at PayPal and JPMC building large-scale onboarding/account setup systems using React/TypeScript with Spring Boot/Node microservices and Kafka. Also built an Ignition-based SCADA monitoring tool at Mainspring Energy that became the default for manufacturing/test engineers by aggregating real-time telemetry and historical test data.”

“Backend-focused software engineer (4+ years) across e-commerce, banking, and healthcare who owned mission-critical checkout/order management end-to-end and improved peak-traffic success rates via resiliency patterns (timeouts/retries/caching) and data-driven iteration. Also built and shipped real-time operational dashboards (React/TypeScript + Spring Boot) using WebSockets and event-stream integrations, with strong experience in Kafka/RabbitMQ-style messaging at scale.”

“Full-stack engineer (~4 years) who has owned and shipped customer-facing SaaS onboarding and a role-based real-time analytics dashboard using TypeScript/React with a modular backend. Experienced in microservices with RabbitMQ and strong observability practices (correlation IDs, structured logging, queue metrics), and built an internal deployment tracker integrated with CI/CD that replaced manual spreadsheet/Slack processes.”

“Backend engineer focused on cloud/distributed systems, deploying Java 17/Spring Boot microservices on AWS EKS with RDS and Kafka. Demonstrated strong production readiness work (DB lock mitigation, Kafka idempotency, gradual rollouts) and delivered a major latency improvement (~400ms to ~100ms). Also has proven cross-layer troubleshooting skills, isolating intermittent API timeouts to a specific Kubernetes node’s network interface issue, and partners closely with ops teams to build dashboards and workflow automation (including Python scripts).”

“AI/LLM engineer at Capital One who deployed a production RAG-powered fraud analysis and document intelligence platform using LangChain, OpenAI, Pinecone, Kafka, and AWS. Focused on reliability in real-time investigations via hybrid retrieval, schema-validated outputs, and LLM verification loops, reporting review-time reduction from hours to minutes and ~99% fraud detection precision.”

“Built a hackathon "Patient Summary Assistant" backend focused on healthcare workflows, combining RAG-based summarization with HIPAA-minded privacy controls (NER redaction + encryption). Demonstrated strong infra skills by deploying on Kubernetes with Helm/HPA and GitOps (ArgoCD), plus migrating from OpenAI to an on-prem Llama 3 stack (vLLM, quantization, shadow-mode testing) and adding real-time Kafka ingestion for patient vitals/anomaly alerts.”

“Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.”

“Built and deployed a production RAG-based multi-agent chatbot during an internship to help consultants answer client questions and guide users through new IT systems with step-by-step instructions. Demonstrates hands-on experience with LangGraph/LangChain/Google ADK, unstructured document parsing and chunking for RAG, and a reliability-first approach to agent workflows (metrics, fallbacks, human-in-the-loop, guardrails).”

“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”

“ML/LLM practitioner with production experience building a healthcare review sentiment pipeline (RateMDs) using Hugging Face Transformers plus a LangChain+FAISS RAG layer for interactive querying. Also led orchestration-driven optimization of Nike’s Fusion ETL pipeline, improving runtime efficiency by 20%, and has experience translating ML outputs into Tableau dashboards for non-technical healthcare stakeholders (e.g., readmission risk).”