Vetted Kubernetes Professionals

“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”

“CIO-level technology leader (most recently at Sovos) who owned the full tech roadmap across product, infrastructure, and corporate IT, scaling engineering across 14 countries with an architectural review board and standardized security/observability. Hands-on in high-severity incidents (ransomware) while managing executive/client communications, and drove a reported 40% product-velocity lift by adopting AI code assistants and agentic AI (Devin) alongside Kubernetes + Bottlerocket for secure scalability.”

“Software engineer with experience across Goldman Sachs, BYU Broadcasting, Juniper Networks, and an edtech startup (Doubtnut), spanning data migrations, AWS-based media backends, and microservices observability. Built a Redis/ElastiCache caching layer in front of DynamoDB/S3 to improve media delivery latency and cost, and created an SEO indexing automation tool using the Google Search Console API that saved ~15–30 person-hours per day.”

“Built and shipped a production agentic LLM tool that helps internal teams update technical product whitepapers using plain-language edit requests, with strong guardrails (citations, verification, refusal/clarify flows) to reduce hallucinations and maintain compliance. Experienced taking LLM workflows from rapid LangChain prototypes to more predictable, debuggable LangGraph agent graphs, and orchestrating end-to-end ingestion/embedding/indexing/eval/deploy pipelines with Kubeflow.”

“Software engineer with ~2 years at UnitedHealth Group plus CMU coursework/TA experience, spanning backend modernization and cloud-native operations. Worked on migrating a customized open-source EDI system from Python 2 to Python 3 while improving SQLite database traceability via JSON exports, and has hands-on Kubernetes microservices deployment on Azure using Helm, HPA, and Jenkins-based Git-triggered CI/CD. Also built a large-scale real-time ride-hailing simulation using Kafka + Samza with explicit fault-tolerance strategies.”

“Backend/platform-focused engineer who builds scalable onboarding and data ingestion pipelines for complex client data formats, emphasizing staged validation, idempotent job boundaries, and safe rollouts behind feature flags. Strong in production diagnostics (Kibana/Logstash, SQL, debugger traces) with a concrete example of finding a regression causing incorrect Tax Loss Harvesting alert counts within a day, and experienced enabling both engineers and customer-facing teams through docs, runbooks, and technical walkthroughs.”

“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”

“Engineering leader (12+ years Director, 17 years lead) focused on developer productivity and platform/framework work across Oracle, PlayStation, Workday, and CafePress. Notable for building distributed teams from scratch and delivering high-impact platform architecture—e.g., re-architected PlayStation’s upload pipeline to support 500GB–5TB submissions using browser-to-AWS chunked uploads with SNS/SQS and deduplication/resume support.”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

“Built and deployed a production LLM-powered clinical support assistant at BJC HealthCare (RAG + transformer) to answer patient questions, summarize clinical notes, and support appointment workflows. Implemented PHI-safe data pipelines (Spark/Hadoop/Kafka) with automated scrubbing, dataset versioning, and audit logs, and runs the system on Docker/Kubernetes with Pinecone vector search while partnering closely with clinical operations staff.”

“Amazon Alexa engineer who architected and shipped a GenAI Knowledge Agent used by 2M+ customers, focused on making LLM outputs auditable via citations and a verification layer that prevents hallucinations. Built the full vertical slice (FastAPI/LangChain backend + React/TypeScript streaming UI) while keeping p99 latency under 200ms, and has proven incident response experience on AWS (Lambda/DynamoDB scaling issues).”

“AWS intern who significantly evolved a Drift Audit Service backend (Control Tower/EventBridge context) to make drift findings more explainable and reduce false positives by adding a verification step in Lambda before event ingestion. Demonstrates strong API design fundamentals in Python/FastAPI (contracts, idempotency, security controls) and careful rollout practices (feature flags, canaries, phased deployments).”

“Early-career software engineer who owns revenue-critical invoice processing and internal ops tooling end-to-end. Has built TypeScript/React systems backed by MongoDB and Temporal, and designed scalable SQS-based onboarding workflows with FIFO/DLQ monitoring. Notably redesigned an Authzed SpiceDB authorization model, shrinking a 500+ line schema to ~20 lines while meeting sub-100ms p95 latency.”

“Product/technology leader with deep security and cloud infrastructure expertise who drove a major shift from hardware-based networking/security appliances to cloud-native capabilities, growing cloud revenue from $0 to $400M in 4.5 years. Led an innovative eBPF-based approach (“precryption”) to enable lightweight cloud TLS interception/decryption, and has hands-on coding interest (recent Rust work on a personal cybersecurity identity/trust platform).”

“Player-coach engineering leader focused on AWS ML infrastructure and deep learning image delivery: provisioned EKS/Kubernetes for multi-node training and automated image release pipelines (Python + AWS CDK) to cut release time from 2 weeks to 1. Also built customer migration tooling for SageMaker HyperPod and owned a security incident end-to-end, implementing prevention tests and process improvements.”

“Backend/data-platform engineer with Stripe and Salesforce experience focused on global payouts/treasury systems. Built an end-to-end payout settlement monitoring platform (FastAPI microservices, Kafka/Spark streaming, React dashboard, CloudWatch alerting) that cut settlement delays 25% and reconciliation time 30%, and productionized an ML anomaly detection service that reduced missed issues by 30%. Experienced modernizing monoliths into microservices with feature flags/canaries and close partnership with treasury/risk/CTO stakeholders.”

“Robotics software engineer with hands-on ROS2 experience building an audio conversion node and integrating Whisper LiveKit for streaming speech-to-text in a simulated hostile (outer space) robot environment. Also worked on a 2023 LiDAR + ML vision obstacle-detection project for a hospital-nurse-assistant robot, and has strong large-scale CI/CD deployment experience from AWS (2022–2024) across alpha/pre-prod/prod stages.”

“LLM/RAG practitioner who built an AWS-based enterprise document search and summarization platform with RBAC and scaled it to 10K+ users, solving relevance issues via contextual chunking and hybrid retrieval. Also designed agentic workflows for a telecom forecast-validation use case using sub-agents, tool APIs, and strict context management, and has proven pre-sales influence (supported a $300K manufacturing deal with a roadmap-driven pitch).”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

“Built and operationalized an internal LLM/RAG system for engineering specs—starting with an at-home prototype using real ERP documents, then securing hardware, standing up a GPU/software stack, and deploying through UAT to production. Identified organizational gaps (no shared spec repository) and created a queryable RAG database that reportedly cut document discovery from days/weeks to minutes, while also resolving retrieval issues via improved PDF-aware chunking.”

“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”