Candidate Profile — Candidate #1
Position: Modular — Engineering Director, GenAI Enterprise (Remote)
Candidate Location: San Francisco Bay Area
Experience: 35.5 years experience
Relevant Experience & Education Highlights
Extensive AI infrastructure architecture experience combined with customer-facing technical leadership and hands-on production AI deployments positions this candidate strongly for directing Modular's GenAI Enterprise team.
1. Serve as the primary engineering point of contact for enterprise prospects and customers
Dives deep into unique customer challenges through architecting scalable AI and infrastructure solutions.
- Architected networking control plane for exascale-class supercomputers at a high-performance computing firm, enabling hybrid cloud integration and AI/ML-ready infrastructure for government deployments.
- Led architecture and design of networking and security services for software-defined data center platforms at a virtualization software leader, collaborating with stakeholders on distributed systems.
- Delivered health-monitoring tools like Sync-Monitor using Go, NATS, Prometheus, Helm, and GitLab CI for data center infrastructure management at a cloud services provider.
2. Deliver and demonstrate real, measurable value of the Modular platform to customers through targeted production solutions
Builds and deploys production-ready AI inferencing clusters and services with rapid prototyping.
- Built distributed AI inferencing clusters and infrastructure services at a stealth AI startup and edge computing firm.
- Developed high-performance eBPF/XDP kernel modules in C with Prometheus monitoring in Go for low-latency network observability in personal projects.
- Prototyped and delivered Sync-Monitor tool beta/production-ready in under 8 weeks independently, including entity-state verification and auto-reconnect retries.
3. Hands-on experience with production AI workloads including scaling and serving large models
Demonstrates deep expertise in AI infrastructure, GPU systems, and machine learning frameworks.
- Focused on DPU virtualization, networking, security using K8s, IPDK, DPDK, alongside RAN-edge and software-defined vehicle infrastructure as Distinguished Engineer.
- Applied PyTorch, TensorFlow, NumPy, Scikit-Learn, and machine learning algorithms in GPU infrastructure and distributed systems.
- Engineered scalable orchestration and data/control plane architectures for AI/ML workloads in supercomputing environments.
4. 3+ years of management experience with a proven track record of building high-performing collaborative teams
Exhibits engineering leadership across global teams and complex projects.
- Served as VP Engineering/Distinguished Engineer and Senior Principal Engineer/Software Architect, managing infrastructure for edge AI and automotive domains.
- Led teams in agile methodologies, scrum, OKRs, and global distributed systems development.
- Drove architecture from design to delivery for enterprise software platforms, ensuring scalability and integration.
Requirements & Candidate Alignment
| Modular Requirement | Candidate Qualification |
|---|---|
| 7+ Years of experience in a customer-facing technical roles where you solved hard problems alongside clients | 35.5 years total experience including architecting AI infra, supercomputing networks, and data center solutions for enterprise clients |
| 3+ Years of management experience with a proven track record of building high-performing collaborative teams | Proven management as VP Engineering/Distinguished Engineer and Senior Principal Engineer leading distributed AI and infrastructure teams |
| In-depth understanding of the GenAI execution lifecycle, deployment methodologies, along with testing and quality evaluation methodologies | Deep expertise in AI inferencing clusters, K8s operators, Helm deployments, Prometheus metrics, and GitLab CI for production AI workloads |
| Proven ability to translate complex technical concepts into clear, concise, and understandable language that resonates with for both clients and internal teams | Strong communication skills demonstrated through presentations, requirements analysis, and stakeholder collaboration in enterprise architecture roles |
| Demonstrated ability to rapidly prototype and deliver Proofs of Concept (POCs) that win customer confidence | Rapid prototyping with tools like Sync-Monitor delivered in under 8 weeks and eBPF/XDP modules for real-time observability |
| Adept at navigating ambiguity and ability to exercise sound judgment to identify appropriate solutions and tools, while avoiding unnecessary complexity | Navigated complex distributed systems, hybrid cloud, and scalability challenges in supercomputing and edge AI environments |
| Hands-on experience with production AI workloads including scaling and serving large models | Hands-on building distributed AI inferencing clusters, GPU infra, and ML algorithms with PyTorch and TensorFlow |
| Proficiency in Mojo, Python, C++, or CUDA programming languages, plus familiarity with frameworks such as MAX, Pytorch, or vLLM | Proficiency in C++, Python, PyTorch, TensorFlow, NumPy, and Scikit-Learn for AI and GPU workloads |
If you would like to discuss this candidate or other critical roles, here is a link to my calendar to schedule a call:
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