Key Responsibilities

Platform Architecture & Technical Leadership

• Lead and oversee the end-to-end design of a next‑generation Edge‑AI / IoT platform, defining core architecture, data flows, and interfaces across connected devices, on‑prem, and cloud environments.

• Champion adoption of open communication standards and high‑performance computing frameworks across the product ecosystem.

• Establish and maintain technical decision records, architecture guidelines, and a long‑term technical roadmap in coordination with product leadership.

• Anticipate and mitigate systemic technical challenges such as latency, bandwidth limitations, or data‑model integrity before they affect production systems.

Security & Compliance

• Drive architectural decisions to align with advanced security frameworks and industry compliance standards.

• Implement zero‑trust principles, secure‑by‑design patterns, robust encryption, and supply chain security practices across all platform layers.

• Collaborate with internal stakeholders to prepare documentation, system security materials, and technical evidence supporting compliance and certification efforts.

Engineering Excellence & Team Enablement

• Provide technical leadership and mentorship for a globally distributed engineering team; lead design reviews, innovation sprints, and internal knowledge‑sharing sessions.

• Define and uphold engineering quality standards — including API design, container orchestration, CI/CD pipelines, observability, and disaster recovery readiness.

• Partner with technical leads to convert architectural strategies into clear deliverables while ensuring consistent implementation quality.

Stakeholder & Partner Engagement

• Serve as the senior technical representative during solution design and onboarding discussions with enterprise or public‑sector clients.

• Participate in technical evaluations, proposals, and architecture presentations, showcasing strong familiarity with secure, large‑scale system deployments.

• Cultivate trusted relationships with technology partners and industry bodies within the Edge‑AI and connected‑systems domain.

Required Qualifications

• 10+ years of software engineering experience, including at least 4 years in an architecture or principal technical leadership role.

• Proven experience designing and scaling distributed systems with real‑time or near‑real‑time processing needs.

• Expertise with modern messaging and data streaming protocols (e.g., MQTT, Kafka, AMQP, or similar).

• Hands‑on background in AI acceleration frameworks and edge hardware platforms.

• Understanding of hierarchical data‑modeling standards for device abstraction.

• Familiarity with security control frameworks and experience implementing technical compliance measures in system architectures.

• Strong proficiency with containerized, cloud‑native systems (Kubernetes, Helm, service mesh, observability tooling, GitOps).

• Excellent written and verbal communication skills for both technical and non‑technical audiences.

• Ability to collaborate effectively across globally distributed teams.

Nice to Have

• Experience working with regulated industries or compliance programs related to information security.

• Exposure to additional edge computing or embedded communication protocols.

• Background in sectors such as infrastructure, smart devices, transportation, or related public services.

• Contributions to open‑source projects in cloud‑native or edge‑AI technologies.

• Knowledge of network virtualization, private networking, or advanced connectivity solutions for distributed systems.

• Professional certifications in cloud architecture or enterprise frameworks are a plus.

What Can Be Offered

• A mission‑driven opportunity to shape critical digital infrastructure initiatives with meaningful social and technical impact.

• Full ownership of architectural design decisions in a greenfield platform.

• Competitive global compensation structure including base pay, equity, and performance incentives.

• Fully remote, flexible work culture with periodic on‑site collaboration events.

• Access to advanced computing and AI resources for experimentation and R&D.

• Dedicated budget for learning, conferences, and industry participation.