National Lead - Technology as a Business

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

• 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.

• 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).

• 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

• 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.

• 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).

• 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.

• 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.

• 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.

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

• 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

• Platform-first thinking (reuse > rebuild).
• Strong decision-making under ambiguity.
• Ability to align business, product, and engineering.
• High ownership and accountability mindset.