AI Model Orchestration & Optimization
We design and deploy end-to-end AI inference and training pipelines tailored to your hardware and workload requirements.
- Model selection, quantization, and fine-tuning for edge and server deployment
- Multi-model orchestration with load balancing and failover
- Observability dashboards for latency, throughput, and token metrics
- Integration with vLLM, Ollama, TGI, and custom serving frameworks
Self-Hosted Tools & Automation Scripting
Custom automation and internal tooling that eliminates manual toil and accelerates your engineering workflows.
- CI/CD pipeline design and optimization (GitHub Actions, GitLab CI, Jenkins)
- Infrastructure-as-Code with Ansible, Terraform, and custom Bash/Python tooling
- Internal developer platforms and self-service provisioning portals
- Log aggregation, alerting, and incident response automation
ROCm & GPU Tuning for Real-World Deployment
Production-grade GPU computing on AMD hardware with performance that matches or exceeds proprietary stacks.
- ROCm stack installation, validation, and driver management
- Memory and compute profiling for inference and training workloads
- Multi-GPU and multi-node scaling strategies
- Framework compatibility testing (PyTorch, TensorFlow, ONNX Runtime)
Docker, VM & Systemd Integration Workflows
Reliable, reproducible deployment pipelines from container to bare metal.
- Docker Compose and Kubernetes deployment patterns
- Systemd service unit design with health checks and auto-restart policies
- Virtual machine provisioning with libvirt/QEMU and cloud-init
- Network configuration, firewall hardening, and TLS termination