Lead Architect

Key Responsibilities

Platform Architecture & Technical Leadership

• Lead and oversee the end-to-end design of a nextgeneration EdgeAI / IoT platform defining core architecture data flows and interfaces across connected devices onprem and cloud environments.

• Champion adoption of open communication standards and highperformance computing frameworks across the product ecosystem.

• Establish and maintain technical decision records architecture guidelines and a longterm technical roadmap in coordination with product leadership.

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

Security & Compliance

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

• Implement zerotrust principles securebydesign 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 knowledgesharing 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 publicsector clients.

• Participate in technical evaluations proposals and architecture presentations showcasing strong familiarity with secure largescale system deployments.

• Cultivate trusted relationships with technology partners and industry bodies within the EdgeAI and connectedsystems 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 realtime or nearrealtime processing needs.

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

• Handson background in AI acceleration frameworks and edge hardware platforms.

• Understanding of hierarchical datamodeling standards for device abstraction.

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

• Strong proficiency with containerized cloudnative systems (Kubernetes Helm service mesh observability tooling GitOps).

• Excellent written and verbal communication skills for both technical and nontechnical 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 opensource projects in cloudnative or edgeAI 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 missiondriven 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 onsite collaboration events.

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

• Dedicated budget for learning conferences and industry participation.