Vetted GitOps Professionals

“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”

“Backend/platform engineer who has owned real-time reporting and streaming analytics systems end-to-end, combining FastAPI/Postgres APIs with Kafka consumers, Celery background jobs, and Redis caching. Strong DevOps/GitOps experience deploying Python/Node microservices to AWS EKS with Helm, ArgoCD/FluxCD, and CI pipelines, and has supported phased on-prem to AWS migrations using Terraform and traffic cutovers.”

“IBM Power/AIX infrastructure engineer who has owned large-scale Power9/AIX 7.x estates across primary/DR (VIOS/HMC/vHMC) and has deep hands-on experience with DLPAR, shared processor pool governance, and PowerHA recovery. Also brings modern DevOps/IaC capability—built Azure DevOps/GitHub CI/CD for Terraform + Kubernetes/Helm with strong security controls and resolved a production Terraform state-lock failure by redesigning backend locking and pipeline concurrency.”

“Startup-focused infrastructure/security leader who stepped into head of engineering and re-platformed an entire product end-to-end in 3 months to meet launch. In crypto/fintech, recognized the market-data system as an ETL/data product and rebuilt it as a separable, securely accessible platform—prompting inbound interest within a week—while advocating an open-source-first observability stack (Prometheus/Grafana/Loki) to avoid vendor lock-in.”

“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”

“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”

“Backend engineer at Samsara with prior Amazon internship experience; built an internal Python chatbot for documentation discovery with a full security model (authn/authz and credential-based response filtering) in a sensitive environment. Currently works on dashcam/device-backend systems, including feature-flagged migrations for audio alert behavior and resilient real-time gRPC streaming under unreliable cellular connectivity, while actively monitoring Buildkite/Toolshed CI/CD rollouts.”

“Backend/cloud engineer with deep Azure and distributed-systems experience: owned an end-to-end Python multi-orchestrator config generator (Contrail) spanning OpenStack/vCenter/OpenShift/Kubernetes via a translation-layer approach. Built GitOps-style ARM-template infrastructure and CI/CD with automated testing, including a zero-downtime Databricks-to-Synapse migration using parallel production validation. Worked on Microsoft Azure Identity gateway (reverse proxy for auth) and executed ring-based deployments for major platform migration.”

“Former Amazon intern who owned an end-to-end computer vision system to detect package anomalies in fulfillment centers, from data collection/labeling to production deployment on AWS (EC2/S3) with a Streamlit live-monitoring dashboard. Also has ML-in-production experience deploying and updating a recommendation model on Kubernetes (Minikube) with CI/CD via GitHub Actions, plus prior SDE experience with Jenkins-based pipelines and on-prem to AWS migration work using Glue.”

“Backend/platform engineer with experience at Microsoft, Uber, and Gusto building production AI-agent automation systems in Python (AutoGen) and cloud-native microservices on Kubernetes across AWS/Azure. Has delivered zero-downtime migrations and high-throughput real-time streaming pipelines (Kafka/WebSockets/Redis), and is strong in GitOps/ArgoCD-driven CI/CD with reliable rollouts and rapid rollback.”

“DevOps/SRE focused on cloud-based distributed systems, with strong hands-on Kubernetes production experience (microservices deployments, Helm, probes, resource tuning, CI/CD and Docker build standardization). Demonstrated end-to-end troubleshooting across application, infrastructure, and networking layers—e.g., isolating degraded storage via node disk I/O metrics and restoring performance by draining the node and replacing the volume. Builds Python automation for operational reliability, including scheduled Kubernetes secrets rotation integrated with an external secret manager.”