Python Backend & Platform Engineer
Building reliable APIs, cloud platforms, delivery pipelines, and observable systems.
I work across backend and platform engineering, turning product requirements into production-ready services and infrastructure. My experience includes Python APIs, cloud deployments, container orchestration, infrastructure as code, CI/CD, data pipelines, observability, and applied AI infrastructure.
I care about systems that are understandable, measurable, secure, and easy to operate.
- Backend engineering: REST APIs, GraphQL integrations, microservices, authentication, and database performance
- Cloud and platform: AWS, Azure, Docker, Kubernetes, Helm, and Terraform
- Delivery engineering: Jenkins pipelines, repeatable environments, deployment automation, and rollback workflows
- Reliability and observability: Prometheus, Grafana, ELK, CloudWatch, and OpenTelemetry
- Data and AI infrastructure: PostgreSQL, Cassandra, Kafka, Dagster, LangChain, Ollama, and BitNet
- Reduced deployment time by 40% through improved Jenkins CI/CD workflows
- Reduced infrastructure provisioning time by 60% with Terraform automation
- Migrated 15 Docker workloads to Kubernetes with Helm-based deployment and rollback management
- Contributed to 35% lower AI inference costs by enabling CPU-based BitNet execution
- Built observability across metrics, logs, and traces for distributed workloads
- Designing dependable Python services and deployment workflows
- Deepening Kubernetes, cloud reliability, and infrastructure automation skills
- Building public projects that demonstrate production-style backend and platform engineering
- Exploring remote opportunities in backend, platform, DevOps, cloud, and SRE teams
I enjoy discussing backend architecture, Python, cloud infrastructure, Kubernetes, observability, and practical engineering trade-offs.