Vetted Apache Kafka Professionals

“Full-stack/backend engineer who owned end-to-end delivery for DentalScan, an AI-powered healthcare platform, building Kafka-based Node.js microservices plus Next.js clinician dashboards and CI/CD on Vercel. Productionized LLM/voice AI patient communication (Twilio/Retell AI) with prompt templates, guardrails, and monitoring, supporting 1,000+ scans/day at sub-100ms latency and reducing clinic admin workload. Previously modernized an agricultural data platform at Infosys to Spring Boot/Docker microservices with Redis, Jenkins CI/CD, and AWS CloudWatch monitoring.”

“iGaming product leader with 11 years of Product Owner experience, spanning QA automation (built a testing framework) through BA and ultimately PO for an online poker team for nearly 6 years. Has shipped and managed live web/mobile/tablet games and worked closely with compliance/UKGC on game certification and launches, using BI/data and player feedback to prioritize features and F2P retention mechanics (tournament ticket funnels, quests).”

“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”

“Senior software engineer (backend/infra) with hands-on Unity gameplay feature work, including an editor-time page-generation and multilingual pipeline that renders gamebook pages as textures for instant 3D page flips. Combines immersive UX techniques (mask textures + custom shader hover feedback) with strong multiplayer/networking and scalable infrastructure thinking (latency, prediction, security, autoscaling).”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”