Vetted Kubernetes Professionals

“Software engineer with unusually mature AI-native development practices, using Claude/GPT/Copilot across requirements, implementation, testing, and documentation. At Anzenna, they effectively acted as a tech lead for a multi-agent setup spanning four repos, creating specialized agent workflows and maintaining top-level cross-repo documentation to coordinate complex work.”

“Engineering leader with 8+ years across IoT, emergency messaging, and political campaign data science, including operating a massive IoT microservices platform at several million devices and roughly 200 million requests per minute. Particularly strong in retrofitting legacy systems for modern observability using OpenTelemetry, Honeycomb, and Grafana LGTM, while also managing distributed international teams and developing engineers into senior and tech lead roles.”

“Backend engineer with experience at HSBC and Machinations who has delivered major production performance wins (cutting large trade-file upload times from ~13–15s to ~2s) using chunked parallel processing with strong reliability controls. Also built and shipped an applied AI RAG workflow using Langflow + Cohere embeddings + FAISS with hosted/local LLM fallbacks (Hugging Face, Ollama) and production-grade guardrails, observability, and evaluation.”

“Data science and AI professional with Capital One experience building churn prediction and GenAI-powered document intelligence solutions. Stands out for pairing hands-on technical depth in NLP, LLMs, and analytics with strong business communication, including driving adoption across teams and contributing to a 25% reduction in customer churn.”

Chuangyu Xu

Screened

Entry-Level Software Engineer specializing in Java backend and distributed systems

New York, NY

“Built and shipped a production AI mock-interview platform where an LLM “interviewer” generates role-specific questions, runs multi-turn follow-ups, and outputs structured scoring/feedback using RAG. Demonstrates strong production-readiness practices (Prometheus/Grafana monitoring, retries/timeouts, fallback models/templates, schema validation, replay-based debugging) and achieved 99%+ availability with ~40% higher session completion. Also has experience integrating agents with messy SAP/ERP-style data sources using a data middleware layer, cleaning/validation, and idempotent write safeguards.”

“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”

“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”

Janvitha Mandyam

Screened

Mid-level AI/ML Engineer specializing in Generative AI and NLP

Chicago, IL4y exp

“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”

“Backend/data engineer with hands-on production experience building reliable Python FastAPI services on Kubernetes and delivering AWS EKS + Terraform CI/CD with strong secrets isolation and rollback practices. Also built AWS Glue ETL pipelines into S3/Redshift with schema-evolution handling and data-quality controls, modernized legacy analytics into modular Python services with parallel-run parity validation, and has demonstrated SQL tuning impact (minutes to seconds) plus ownership of batch pipeline incidents end-to-end.”

“GenAI/RAG engineer who has deployed a production policy/regulatory search assistant for a financial client using LangChain + Vertex AI, FastAPI, Docker/Kubernetes, and Airflow-orchestrated data pipelines. Demonstrated measurable impact with 50–60% latency reduction and 70% fewer pipeline failures, plus KPI-driven grounding evaluation (90%+ target) and strong cross-functional collaboration with compliance/business teams.”

“Built and owned an end-to-end RAG-based AI support platform at Mechanize (FastAPI/LangChain/Pinecone/React) with rigorous evals and guardrails, driving 45% fewer support tickets and ~$280K annual savings. Also led a high-risk legacy modernization at Argo AI, incrementally extracting a monolithic Django backend using Strangler Fig + feature flags while supporting 10K+ concurrent users.”

Sean O'Dea

Screened

Executive CTO and software architect specializing in scalable SaaS and cybersecurity

Las Vegas, NV19y exp

“Repeat founder and operator who has built multiple startups, including a social platform, and chose to bootstrap after learning firsthand that investor demand was centered on distribution rather than technology. Brings a candid operator-side understanding of VC, term sheets, and fundraising dynamics, plus experience shipping against real scale at Parlement.”

“Software engineer/QA automation leader with Lowe’s experience owning automation quality strategy for a customer-facing platform supporting large contractor orders. Built TypeScript/React dashboards backed by Spring Boot microservices (MongoDB) and RabbitMQ async messaging, with strong CI/CD test automation and production monitoring (Prometheus/Grafana). Also created an internal automated test reporting dashboard that improved QA workflow through training-led adoption and iterative refinement.”

“Backend engineer with banking-domain experience building production microservices and Kafka-based transaction processing systems. While they have not yet shipped a full LLM agent, they bring relevant distributed-systems expertise in reliability, observability, idempotency, and event-driven workflow design, including a reported 15-20% performance improvement.”