Lead AI Architect

Lead Architect - Public Sector

Location: Remote USA

Mode of Hire: Fulltime

We are building a next-generation Vision AI software platform designed specifically for US-based public sector clients — spanning federal agencies, defense contractors, smart-city programs, and critical infrastructure operators. As our Lead Architect / Head of Architecture, you will be the technical authority shaping every layer of the platform: from constrained edge devices streaming sensor data via MQTT, through GPU-accelerated inference running on NVIDIA hardware, to the cloud-side orchestration and data pipelines that give our clients actionable intelligence.

You will work at the intersection of systems design, security engineering, and product strategy, translating complex government requirements into elegant, scalable architecture — and then ensuring a globally distributed engineering team executes against that vision with coherence and velocity.

Key Responsibilities

Platform Architecture & Technical Leadership

·         Own the end-to-end architecture of the Edge AI / IoT platform, defining reference designs, component boundaries, data flows, and integration contracts across edge, on-premises, and cloud tiers.

·         Drive adoption of open standards (MQTT, VSS, OPC-UA, ONVIF, etc.) and NVIDIA ecosystem tooling (CUDA, TensorRT, Jetson, NGC) across the product.

·         Establish and govern architecture decision records (ADRs), technology radar, and technical roadmap in close collaboration with product management and the CTO.

·         Identify and mitigate systemic technical risks — latency, bandwidth constraints, model drift, security boundaries — before they become production incidents.

Security & Compliance

·         Lead the architecture design to achieve and maintain NIST SP 800-171 compliance, with a clear pathway toward CMMC Level 2 (and Level 3 where required by contracts).

·         Define zero-trust principles, secure-by-design patterns, cryptographic key management, and supply chain security practices for all platform components.

·         Collaborate with our compliance and legal teams to produce architecture documentation, system security plans (SSPs), and evidence packages required during government procurement.

Engineering Excellence & Team Enablement

·         Serve as the primary technical mentor for a globally distributed engineering team spanning multiple time zones; run architecture reviews, design sprints, and internal tech talks.

·         Define and enforce engineering standards: API design, containerization and orchestration (Kubernetes), CI/CD pipeline quality gates, observability, and disaster recovery.

·         Partner with engineering leads to decompose the architecture into actionable epics and ensure architectural intent is preserved through implementation.

Client & Stakeholder Engagement

·         Act as the senior technical point of contact during pre-sales and onboarding with US public sector clients, translating customer mission requirements into platform capabilities.

·         Participate in government-facing technical evaluations, RFP responses, and architecture briefings — demonstrating deep understanding of federal IT environments (FedRAMP-adjacent infrastructure, air-gapped deployments, etc.).

·         Maintain trusted relationships with technology partners including NVIDIA, cloud providers, and standards bodies relevant to the IoT / edge AI space.

Required Qualifications

·         10+ years of software engineering experience, with at least 4 years in a principal architect, staff engineer, or equivalent technical leadership role.

·         Proven track record designing large-scale distributed systems with real-time or near-real-time constraints — IoT telemetry pipelines, sensor fusion, or similar.

·         Deep expertise with MQTT (broker architecture, QoS, retained messages, MQTT over TLS) and at least one complementary messaging/streaming technology (Kafka, AMQP, ROS 2, etc.).

·         Hands-on experience with the NVIDIA ecosystem: CUDA development, TensorRT optimization, Jetson or similar edge AI hardware, and NVIDIA N/ Triton Inference Server.

·         Solid understanding of Vehicle Signal Specification (VSS) or comparable hierarchical signal / data modeling standards for edge device abstraction.

·         Practical knowledge of NIST SP 800-171 controls and experience translating them into concrete architecture and engineering requirements.

·         Proficiency in containerized, cloud-native architectures: Kubernetes, Helm, service mesh, GitOps, and modern observability stacks (OpenTelemetry, Prometheus, Grafana).