Vetted Kubernetes Professionals

“Software engineer with a rare mix of early startup product-building and large-scale enterprise data platform work. At a 40-person digital signage startup, they wrote much of the customer-facing software, and later at Qualtrics built Go and Python ETLs, internal configuration tools, and B2B data products supporting survey analytics and custom data science workflows.”

“Enterprise cloud/SaaS customer success leader who owned a strategic $102M Azure commitment end-to-end, rescuing a Kubernetes migration by formalizing Sev A/B triage/escalation ownership and executive governance. Drove measurable outcomes including restored executive confidence and consumption growth from $1M to $2M/month (~30% adoption growth) by tying stabilization milestones to migration velocity, forecasting, and expansion strategy.”

“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Amazon engineering leader (15+ years) targeting Senior Manager/Director roles, with deep ownership of contact-center latency and reliability initiatives. Shipped a global production improvement cutting call latency 30–40% and led a complex Citrix SDK integration, including incident response and a backward-compatible rollout strategy to protect existing customers while enabling new features.”

“Meta engineer who has shipped both user-facing full-stack product work and internal AI agent systems at production scale. Most notably built AuditFixer, an agentic remediation pipeline that fully automated audit-fix workflows, cut turnaround from 6-7 hours to under 1 hour, and has already produced 30+ landed diffs, while also owning the Llama API evaluation flow launched for thousands of developers.”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

“Engineering leader who built and scaled a distributed team (Serbia + US) to deliver an internal multi-tenant Kubernetes-based PaaS, moving a large org from manual ops-driven deployments to automated CI/CD with >99.97% uptime and 100% service adoption. Known for culture change (blameless post-mortems, clear intake via ticketing) and security-first platform practices (tenant isolation, Falco) supporting SOC2 compliance; also operates at exec level with stakeholder communication and fundraising.”

“Platform/backend engineer focused on Kubernetes at scale: built a Java control-plane service for multi-region cluster provisioning/monitoring/upgrades using Kafka-driven async workers, and solved peak-load provisioning failures by eliminating blocking I/O and dynamically scaling consumers. Also shipped an LLM-assisted Kubernetes troubleshooting/remediation feature that pulls Prometheus logs/metrics into prompts and uses guardrails (confidence thresholds + human-in-the-loop) to prevent risky actions.”