Vetted Kubernetes Professionals

“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”

“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

“Robotics software engineer who built a full autonomous navigation pipeline on TurtleBot3 in ROS2 from bring-up and SLAM through custom A* planning, obstacle avoidance, and frontier exploration—without relying on Nav2. Demonstrated strong debugging and performance optimization (QoS fix with AMCL; reduced exploration mapping time from ~12 to <7 minutes) and brings 2+ years of HPE cloud/edge deployment experience with Docker/Kubernetes/Helm and GitHub Actions.”

“Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”

“Robotics-focused software engineer/technical lead with Google/Android engineering experience, building automation and robotic framework architecture in C++/Python. Has hands-on experience with ROS/ROS2, SLAM/localization/mapping, motion planning, and scaling distributed robotics nodes using Docker/Kubernetes with CI/CD (Jenkins), plus REST API-based integration testing and validation.”