AI Architect

We are looking for a visionary Lead Architect to bridge the gap between business intent and technical execution. Your primary mandate is to move beyond tactical AI implementation to design and build the overarching strategy, architecture, and platform foundations for an AI-native BSS ecosystem.

Lead the strategic design and integration of AI/ML frameworks into modern, cloud-native BSS platforms to drive operational efficiency and personalized customer experiences.

• Strategic Architecture &
  
  Roadmap:
  
  Define the long-term AI/ML strategy and platform architecture. You will be responsible for building a scalable foundation that supports Generative AI, predictive analytics, and autonomous service orchestration.
• AI Ecosystem Design:
  Architect end-to-end AI solutions, including real-time data ingestion, feature stores, and vector databases to support RAG (Retrieval-Augmented Generation) patterns.
• Agentic Orchestration:
  Design multi-agent workflows that replace static decision trees with AI models capable of reasoning about business outcomes (e.g., autonomous order fallout remediation).
• Domain Integration:
  Embed AI into core BSS domains such as CPQ (product configuration), CRM (personalised proposals), and Revenue Management (anomaly detection for billing).
• Standardisation:
  Align all AI implementations with the TM Forum AI-Native Blueprint and Open Digital Architecture (ODA) to prevent siloes.
• Governance & Ethics:
  Establish frameworks for AI Governance, ensuring model transparency, bias mitigation, and compliance with global data regulations like GDPR.
• Legacy Modernisation:
  Architect "AI-powered overlays" to modernise legacy BSS systems without requiring immediate, high-risk "rip-and-replace" transformations.

• Strategic Leadership:
  Proven experience building AI/ML strategies and platform architectures from the ground up within a telecommunications or enterprise software environment.
• AI/ML Frameworks:
  Proficiency in Tensor Flow, PyTorch, or Hugging Face; deep experience with Large Language Models (LLMs) and Lang Chain.
• Data Engineering:
  Hands‑on experience with MLOps pipelines, feature stores, and data orchestration tools like Kafka or Airflow.
• Cloud Infrastructure:
  Experience deploying AI at scale on platforms like AWS Bedrock or Azure ML.
• Industry Frameworks:
  Deep familiarity with TM Forum GB1082 (AI for BSS) and IG1421 (MAS Ontology Model).