Vetted Kubernetes Professionals

“Backend engineer who built and launched a warehouse locations/inventory microservice for Walmart, supporting a new product rollout with on-call war-room ownership and now running across all US distribution centers. Emphasizes reliability and correctness (background syncs, 2PC concepts, alerting) plus design-first API development in Python/FastAPI with OAuth/JWT and RBAC, and has led staged legacy-to-microservice migrations with continuous data integrity verification.”

“Backend/data platform engineer who has built and operated a cloud-native media ingestion/processing platform in Python (Django/DRF, FastAPI) with Kafka, Postgres, and Redis, emphasizing multi-tenant security and reliability. Delivered AWS production systems combining EKS and Lambda with Terraform + GitHub Actions/Helm, and built Glue-based ETL pipelines with strong schema-evolution and data-quality practices; also modernized SAS analytics into Python on AWS. Seeking fully remote roles with a $120K–$140K base range.”

“Backend engineer with hands-on experience modernizing high-volume transactional systems by decomposing monoliths into Spring Boot microservices on AWS, using Kafka for async workflows and Redis/SQL tuning for latency. Has built Python/FastAPI services with strong API contracts and production-grade security (OAuth2/JWT, RBAC, row-level security), and proactively hardened payment flows against race conditions and double-charging via idempotency.”

“Built and shipped a production LLM-powered RAG system at Upstart enabling natural-language search across 50k+ scattered internal technical docs. Delivered sub-300ms p95 latency for ~50 active users with strong hallucination safeguards (retrieval-first, thresholds, citations) plus robust testing/monitoring and cost controls (prompt caching cutting API spend ~20%).”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“GenAI engineer who has deployed production LLM/RAG chatbots for internal document search, focusing on reliability (hallucination reduction via prompt guardrails + retrieval filtering) and performance (latency improvements via caching). Experienced with LangChain/LangGraph orchestration for multi-step agent workflows and iterates using monitoring/logs and benchmark-driven evaluation while partnering closely with product and business teams.”

“AI/LLM practitioner at iFrog Marketing Solutions who drove a RAG chatbot from prototype to production in a legacy, AI-resistant environment by validating customer needs and building a business case. Implemented production-grade LLM practices (CI/CD eval gating, rollbacks, prompt/context engineering) and led internal workshops to bring non-AI-native developers up to speed while partnering with sales on tailored demos to drive adoption.”

“Backend/web API engineer with hands-on experience deploying .NET Core APIs to Azure App Service and stabilizing production systems through disciplined log-driven troubleshooting, environment configuration management, and SQL performance tuning (execution plans, query rewrites, indexing). Has also debugged cross-layer incidents involving DB locks and network latency, and modifies Python/XML automation scripts to meet customer-specific requirements while improving logging and performance.”

“Software engineer who built and owned a centralized Asset Management platform at Hyundai Mobis used daily by multiple internal teams, delivering everything from UI/TypeScript React frontend to Spring Boot services, databases, deployment, and post-release support. Experienced in scaling microservices with Kafka-based async workflows, improving data consistency/real-time updates, and implementing reliability patterns like standardized API contracts, retries, DLQs, and targeted caching.”

“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”

“Software engineer with experience across data engineering and backend/platform work: owned a Databricks/PySpark real-time pipeline powering customer dashboards with a 15-minute SLA, and helped modernize an investor web app from JSP to React/TypeScript with API + SQL/materialized-view performance improvements. Also contributed to breaking a Java monolith into microservices (Redis + gRPC on AWS EKS) and built an EC2-deployed Play Store/App Store crawler that reduced third-party data costs.”

“Backend engineer focused on AI-enabled systems, having built a production-style RAG pipeline (vector search + LLM) exposed via Python/Flask endpoints with strong observability and hallucination-reduction techniques. Demonstrates deep performance work in PostgreSQL/SQLAlchemy (5x faster analytics queries) and high-throughput optimization using Celery + Redis (800ms to 120ms latency, 3x throughput), plus schema-per-tenant multi-tenancy with tenant-aware middleware and logging.”

“Backend-focused Python engineer who owned and deployed EcoHero, a recycling guidance app using FastAPI + Firebase with barcode lookup, ZIP-code-based state rules, and user history tracking backed by 50 state datasets. Has hands-on Kubernetes + Docker experience and uses GitHub Actions and GitOps-style PR workflows for consistent deployments, plus event-driven async processing patterns with idempotency and retries.”

“Software engineer experienced owning internal, customer-facing dashboards and internal ops tools end-to-end, emphasizing fast iteration without sacrificing stability (CI/CD, automated tests, feature flags, monitoring). Built a TypeScript/React role-based dashboard backed by Java Spring Boot and has hands-on microservices experience with RabbitMQ, including production hardening with retries, dead-letter queues, logging, and health checks.”

“Software engineer with deep healthcare claims domain experience who has owned customer-facing portals end-to-end (Java/Spring Boot + React/TypeScript) and improved usability/performance based on real user feedback. Built microservices using REST and RabbitMQ with strong observability (Splunk/cloud metrics), and delivered an internal claims investigation dashboard that streamlined operations through centralized data, search, and filtering.”

“Robotics software engineer who built the software layer for an autonomous warehouse sorting system, spanning navigation/path planning, task scheduling, and backend services. Deep hands-on ROS 2 Foxy experience (Nav2/costmaps) and real-time multi-robot debugging, using simulation-driven analysis plus incremental/partial re-planning to handle dynamic obstacles in production-like warehouse environments.”

“Software engineer with BitSight experience owning and revitalizing a critical internal Entity Management Portal (Django/React), clearing 30+ backlog items and boosting internal workflow efficiency ~40% through performance re-architecture (Redis caching) and disciplined testing. Also built a collaborative chore management platform (React/FastAPI) emphasizing responsiveness (optimistic UI) and scalability (connection pooling, Docker), and improved microservices security by centralizing secrets management with AWS Secrets Manager across multi-cloud environments.”

“Built and deployed a production real-time voice AI support agent that answers inbound calls, identifies callers, troubleshoots via a knowledge base, and automatically creates/updates tickets with escalation to humans when needed. Demonstrates strong reliability/latency engineering (streaming, schema validation, idempotency, DB constraints) and uses LangGraph state machines plus OpenAI Agents SDK for multi-agent routing, with KPI-driven testing and monitoring.”

“Built a college AI chatbot during a master’s program, owning the full Python/Flask backend plus Google Gemini integration and a Postgres persistence layer (course info + conversation history), including caching/performance tuning. Also deployed and migrated ETL/ELT workloads from AWS Lambda into Kubernetes/EKS with GitHub Actions-based GitOps CI/CD, IRSA permissions, and Secrets Manager/S3/Postgres connectivity.”

“Backend/ML systems engineer who built a Flask + PostgreSQL internal ticketing platform and demonstrates strong database/ORM performance depth (indexes, partitioning, RLS multi-tenancy). Notably optimized a high-throughput attachment OCR/embedding pipeline with batching, deduplication, and Redis caching, cutting median latency from 45s to 10s and reducing worker cost by 35% while increasing throughput 4x.”

“Built and productionized a recommendation system from notebook prototype into a low-latency, scalable Cloud Run service using Docker, FastAPI, Terraform, CI/CD (GitHub Actions), and MLOps tooling (Vertex AI, MLflow). Experienced diagnosing real-time workflow issues using structured logging/ELK and GCP metrics, including resolving intermittent 504s by fixing unbounded SQL and adding caching. Also partners with sales/customer teams (Wasabi) to deliver tailored demos, troubleshoot, and drive onboarding/adoption.”

“Software/full-stack engineer focused on deploying and integrating microservice applications into production AWS and hybrid cloud/on-prem industrial environments. Demonstrated end-to-end troubleshooting by tracing intermittent user failures to network routing/packet loss caused by load balancer and NIC misconfiguration, then adding monitoring to prevent recurrence. Also delivers customer-specific Python extensions with strong validation, testing, and backward compatibility.”

“Full-stack engineer with hands-on experience building real-time applications (Socket.io chat app) and data-heavy systems in banking/loan management. Comfortable across React and backend services (Spring Boot/Node), with a focus on scalable API design, database performance (indexing/pagination/caching), and deployment via CI/CD and cloud infrastructure.”