National Lead - Technology as a Business

Job description

**Job Purpose** This role is responsible for translating business and product requirements into scalable cloud-native architectures, ensuring real-time performance, reliability, compliance, and cost efficiency while leading multiple AI and engineering teams building reusable PaaS capabilities rather than one-off solutions. **Duties and Responsibilities** **A. Platform Architecture Ownership (Primary)** - Own the reference architecture for the Voice AI platform across: - Tenant management - Real-time voice runtime - AI orchestration - Telephony abstraction - Compliance audit layers - Design and evolve multi-tenant SaaS architecture with: - Tenant isolation (config, data, runtime) - Shared core services - Per-tenant policy enforcement - Ensure platform supports configuration-driven agent creation, not code-heavy customization. **B. Cloud SaaS Engineering Leadership** - Lead cloud-native design on Azure, including: - Kubernetes (AKS), microservices, event-driven systems - API Gateway, WebSockets, WebRTC, NGINX - Redis, Kafka/Event Hubs, Blob/Vector storage - Define SaaS-grade non-functional requirements: - Availability, scalability, latency, DR - Tenant-level throttling and quotas - Usage metering and billing hooks - Drive cost-aware architecture decisions (compute, LLM usage, speech infra). - Own environment strategy (dev / test / prod, tenant-scoped). **C. Real-Time Voice AI Orchestration** - Ensure ultra low latency - Architect deterministic + AI hybrid flows: - State machines / orchestration controlling AI calls - Guardrails around compliance-critical steps - Design failure-resilient voice flows: - No mid-call drops - Graceful degradation - Fallback logic **D. Delivery, Quality Reliability** - Translate architecture into clear execution plans for GB06 leads. - Review and approve: - Architecture diagrams - API contracts - Data flows - Runtime decisions - Own production readiness: - Observability, metrics, alerts - Conversation replay - Incident response patterns - Ensure backward compatibility and controlled platform evolution. **E. Compliance, Security Governance** - Ensure platform meets financial services compliance: - Consent, disclosures, call recording - PII masking and access control - Architect audit-first systems: - Every call traceable - Deterministic logs alongside AI outputs - Drive Responsible AI practices: - Explainability - Bias checks - Model/version governance - Own fraud spoofing architecture (voice biometrics, replay detection). **F. People Technical Leadership** - Lead and mentor across AI, Core Platform, Telephony, QA. - Raise architectural maturity across teams. - Own hiring and capability building for: - Platform engineers - AI engineers with production mindset - Act as final technical escalation point. **Key Decisions / Dimensions** - SaaS vs tenant-specific customization boundaries. - Cloud architecture patterns and technology choices. - Platform capability roadmap and deprecations. - Model orchestration and runtime strategies. - Cost vs performance trade-offs. **Major Challenges** - Building a single platform that serves diverse enterprise use cases without fragmentation. - Maintaining real-time guarantees while integrating LLM-heavy workflows. - Scaling multi-tenant voice traffic with strict isolation and compliance. - Balancing speed of innovation vs platform stability. - Preventing architecture sprawl as teams grow. **Required Qualifications and Experience** Bachelors or Masters degree in Computer Science, Engineering, or related field. - 14+ years in software / platform engineering. - 5+ years owning cloud-native SaaS or PaaS architectures. - Proven experience building enterprise-scale, multi-tenant platforms. - Experience in real-time systems (voice, video, streaming) strongly preferred **Technical Skills** - Strong system architecture design skills (HLD/LLD). - Deep experience with: - Azure (AKS, networking, security, managed services) - Microservices, event-driven architecture - API gateways, WebSockets, WebRTC - Working knowledge of: - AI/ML LLM-based systems (not research, but production usage) - Speech pipelines (STT, TTS) - Strong understanding of SaaS operational concerns: - Billing, metering, quotas - Observability and SRE principles **Leadership Behavioural Skills** - Platform-first thinking (reuse > rebuild). - Strong decision-making under ambiguity. - Ability to align business, product, and engineering. - High ownership and accountability mindset.