Responsibilities

● Define and drive Velaura’s engineering infrastructure requirements covering compute, storage, job scheduling, security, backup, disaster recovery and cloud in partnership with stakeholders.

● Lead AI adoption across engineering functions including RTL, verification, DFT, physical design, packaging, software, and CAD itself. Identify where AI-assisted workflows create measurable leverage and drive them from pilot to standard practice.

● Own design-flow CI/CD pipelines including orchestration, flow-release qualification, and result/QoR dashboards

● Provide the compute, capacity, and deployment support that functional regressions run on, scaling to thousands of jobs per week; teams own their regression content, triage, and pass/fail criteria, and this role makes sure the infrastructure underneath never becomes the bottleneck.

● Own flow release process end to end: assemble team-owned flow content into qualified releases, regression-test before deployment, and manage versioning, cadence, and rollback — keeping handoffs from RTL through synthesis, place-and-route, and signoff consistent.

● Lead the deployment, and integration of EDA tools across multiple vendors, balancing productivity, quality, flexibility, and cost.

● Own EDA licensing strategy, vendor relationships, procurement, budgets, IP administration, and license compliance.

● Build and operate the platform behind AI-assisted engineering—model access, agentic tooling, compute capacity, and data pipelines—with the security and IP guardrails required to apply AI to proprietary design data safely, measuring productivity impact and retiring what doesn’t pay off.

Required Qualifications

● Deep understanding of the complete ASIC development lifecycle, from architecture and RTL through tapeout—enough to be a credible partner to every functional team.

● Experience owning CAD and infrastructure across multiple successful SoC programs.

● Hands-on experience setting up and maintaining an integrated RTL-to-GDS flow environment—tool and version management, foundry and IP collateral integration, and flow release and qualification across concurrent projects.

● Strong understanding of engineering compute infrastructure: distributed scheduling (LSF, Slurm, or equivalent), storage systems, networking, cloud environments, and license management at scale.

● Hands-on experience with EDA ecosystems from multiple vendors such as Synopsys, Cadence, and Siemens EDA.

● Strong knowledge of version control, CI/CD platforms, and the compute and storage patterns behind high-volume regression workloads, along with reproducible engineering practices.

● Excellent scripting and automation skills using Python, Tcl, Bash, or similar.

● Experience evaluating and deploying AI-assisted engineering tools and workflows, with a practical view of where they create leverage in chip development.

● Excellent communication skills and the ability to influence senior technical leaders across disciplines.

Preferred Qualifications

● Experience scaling infrastructure in an early-stage semiconductor company.

● Experience with advanced technology nodes with leading foundries.

● Experience building or operating agentic AI systems, LLM-based tooling, or AI-native design automation.

● Experience supporting chiplet-based designs or advanced packaging.