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- Build end-to-end AI systems: data → model → deployment → monitoring → continuous improvement
- AI infrastructure & reliability: event-driven microservices, queues/streams, idempotency/DLQ, observability
- Real-time / edge AI: GPU/edge inference optimization (Jetson, TensorRT/ONNX Runtime), latency-driven design
- MLOps / AI-Ops: deployment automation, monitoring/alerting, capacity planning, operational debugging
- Computer Vision: detection, tracking, ReID, multi-camera analytics, document OCR
- NLP / GenAI: multi-turn conversational AI, RAG, agentic workflows
- Languages: Python, Bash
- AI/ML: PyTorch, OpenCV, Hugging Face Transformers
- Serving/Edge: NVIDIA Triton, TensorRT, ONNX Runtime, Jetson
- Infra: Kubernetes (CKA/CKAD), Docker, Prometheus, Grafana
- Data/Messaging: Kafka, RabbitMQ, Redis, PostgreSQL