Forward Deployed AI Sprint & Custom ML Pipeline DeploymentSobhan Bahrami
Embed a senior Forward Deployed AI Architect to design, build, and deploy production-grade AI infrastructure inside your VPC or on-premise environment.
Whether you are deploying specialized Mixture-of-Experts (MoE) models, setting up privacy-preserving Federated Learning clusters, or orchestrating autonomous agent substrates, this engagement takes your AI initiative from architecture to high-throughput production.
What is Included:
Zero-to-One Architecture & Scoping: Deep dive into data pipelines, latency budgets, and security/privacy boundaries.
Custom Model Training & Adaptation: PyTorch DDP/FSDP, DeepSpeed ZeRO-2/3, LoRA fine-tuning, and quantized serving via vLLM/Triton.
Privacy & Federated Learning Infrastructure: Flower (flwr) / NVFlare setup with Differential Privacy (DP-SGD) and secure aggregation.
Autonomous Agentic Substrates: Symbiote-style runtime integration with ReAct reasoning, tool synthesis, and human-in-the-loop governance.
Production Hardening: Auto-scaling Kubernetes deployment, observability (Prometheus/Grafana), and sub-10ms event-driven interfaces.
FAQs
Starting at$220 /hr
Duration4 weeks
Tags
AWS
Docker
GCP
Python
PyTorch
Rust
AI Engineer
Machine Learning Engineer
Service provided by
Sobhan Bahrami Budapest, Hungary
- 6
- Followers
Forward Deployed AI Sprint & Custom ML Pipeline DeploymentSobhan Bahrami
Starting at$220 /hr
Duration4 weeks
Tags
AWS
Docker
GCP
Python
PyTorch
Rust
AI Engineer
Machine Learning Engineer
Embed a senior Forward Deployed AI Architect to design, build, and deploy production-grade AI infrastructure inside your VPC or on-premise environment.
Whether you are deploying specialized Mixture-of-Experts (MoE) models, setting up privacy-preserving Federated Learning clusters, or orchestrating autonomous agent substrates, this engagement takes your AI initiative from architecture to high-throughput production.
What is Included:
Zero-to-One Architecture & Scoping: Deep dive into data pipelines, latency budgets, and security/privacy boundaries.
Custom Model Training & Adaptation: PyTorch DDP/FSDP, DeepSpeed ZeRO-2/3, LoRA fine-tuning, and quantized serving via vLLM/Triton.
Privacy & Federated Learning Infrastructure: Flower (flwr) / NVFlare setup with Differential Privacy (DP-SGD) and secure aggregation.
Autonomous Agentic Substrates: Symbiote-style runtime integration with ReAct reasoning, tool synthesis, and human-in-the-loop governance.
Production Hardening: Auto-scaling Kubernetes deployment, observability (Prometheus/Grafana), and sub-10ms event-driven interfaces.
FAQs
$220 /hr