

Backend and ML systems engineer with hands-on experience building scalable RAG pipelines, telemetry infrastructures, and optimized deep learning models for hardware deployment. Experienced in Python, TensorFlow, PyTorch, CUDA, and PostgreSQL, with a strong focus on performance optimization, reproducibility, and production-ready system design. Successfully improved model accuracy beyond published baselines and enhanced deployment efficiency through quantization and FPGA-aware optimization. Passionate about bridging machine learning research with real-world infrastructure and scalable backend systems.
Languages: Python, SQL/NoSQL, Bash, JavaScript/TypeScript, Java, C/C, HTML/CSS
Backend & Protocols: HTTP/HTTPS, TCP/UDP, SSH/SSHCA, MCP, REST APIs, Webhooks
Testing & Reliability: automated test pipelines, CI/CD testing, integration testing, validation workflows
Telemetry, Monitoring & Observability: OpenTelemetry, Fluent Bit, TimescaleDB, Grafana, Log Enrichment & Routing Pipeline
Development Practices: CI/CD Pipelines, Telemetry Integration, Log Aggregation, Scrum, Data Processing, Documentation Automation
Frameworks & Libraries: FastAPI, PyTorch, PETF, TensorFlow, CUDA, Numba, LangChain
DevOps, Infra & CI/CD: Docker, Kubernetes, Git & GitHub Actions, Jenkins, GCP, Postman, Anaconda, Workflow Automation, JSDoc, Coverage Reporting