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Current Position

AI Infrastructure Engineering Manager — Autonomous Solutions, Inc. (ASI)

August 2026 — Present

Leading AI infrastructure and the data platform powering autonomy and advanced prototyping at a robotics and autonomous-systems company. Responsible for hybrid compute strategy, a company-wide data platform, and the engineering foundations that move research from prototype to production — building and scaling a cross-functional team spanning data, infrastructure, embedded, AI applications, and software engineering. Partnering directly with the CTO and across IT, OT, and R&D to accelerate AI-research-to-product pipelines while deploying safely and with operational rigor.

Three Strategic Fronts

1. Hybrid Compute Infrastructure

Designing and operating a hybrid compute strategy spanning on-premises, cloud, and neocloud (GPU-specialized) providers. Matching workloads — from large-scale simulation to imitation learning and model training — to the right infrastructure and economics, and dramatically compressing provisioning time so research teams can move fast.

2. Company-Wide Data Platform

Building the data platform that turns raw operational data into usable, real-time infrastructure for the entire organization. Grounded in a modern lakehouse architecture (medallion design) that ingests large-scale field data, powers real-time visualization and analytics, and serves every business unit and data type across the company.

3. AI Research-to-Product Pipelines

Aligning IT, OT, and R&D to accelerate the path from AI research to deployed product. Establishing the infrastructure, tooling, and operational practices that let teams experiment quickly while meeting the reliability and safety bar required for autonomous systems in the field.

Technical Vision & Architecture

Infrastructure-First, Research-Accelerating

Building a modern AI infrastructure stack where compute, data, and deployment are designed as one system — so researchers and engineers can iterate quickly and ship reliably. The platform unifies large-scale field data into an accessible, real-time foundation that serves as the backbone for AI across the organization.

Core Architectural Pillars

  • Hybrid Compute: On-prem, cloud, and neocloud orchestration — workload-matched for performance and cost (GPU training and inference, simulation, imitation learning)
  • Data Platform & Lakehouse: Medallion-architecture pipelines on a modern lakehouse, moving large-scale field data to near-real-time availability and visualization
  • Provisioning & IaC: Infrastructure-as-code and streamlined provisioning that cut turnaround from months to weeks
  • MLOps & Deployment: Reproducible, observable pipelines connecting research to production with operational rigor
  • IT / OT / R&D Alignment: Leadership strategy aligning functions to accelerate research-to-product while deploying safely
  • Fiscal Deployment: Cost-aware infrastructure and compute decisions, ROI-driven prioritization of AI investments

Contact & Collaboration

Interested in discussing AI infrastructure, hybrid compute, data platforms, or scaling AI research to production? Please reach out:

Let's build the future of AI infrastructure together.