Artificial Intelligence Expert

Role Overview
As an AI-Expert, you will be the primary strategist and driver for transforming traditional network
operations into an AI-augmented, autonomous ecosystem. You will be responsible for the entire
lifecycle of AI integrationfrom initial solutioning and architectural design to leading
development and managing the ongoing operations of production-grade AI platforms. Your
objective is to leverage Machine Learning and Generative AI to automate troubleshooting,
optimize network planning, and assist in real-time issue resolution across the enterprise.
Key Responsibilities

1. Solutioning & Strategic Design
   AI Roadmap & Strategy: Define and drive the vision for AI-integrated network
   management, focusing on faster incident triage and reduced manual intervention.
   SME Collaboration: Work closely with Network SMEs to translate natural-language
   troubleshooting playbooks and "tribal knowledge" into structured, deterministic AI logic.
   Workflow Orchestration: Design complex, multi-stage diagnostic and operational
   pipelines that move the organization toward a "lights-out" NOC (Network Operations
   Center).
   Predictive Frameworks: Architect systems for anomaly detection, predictive
   maintenance, and capacity forecasting to identify risks before they impact service.
   Governance & Ethics: Ensure all AI solutions maintain high standards of auditability,
   security, and deterministic reliability.
2. Development & Implementation Leadership
   Advanced AI Integration: Lead the implementation of LLM-based solutions, including
   Retrieval-Augmented Generation (RAG) for knowledge management and automated
   resolution procedures.
   Data Science & Engineering: Oversee the normalization and ingestion of massive
   telemetry streams from diverse hardware vendors into unified, actionable data models.
   Automation & Self-Healing: Oversee the creation of "closed-loop" systems that can
   automatically execute remedial actionssuch as failovers or re-routingbased on
   AI-driven insights.
   Stakeholder Tools: Manage the development of intuitive dashboards and interfaces that
   deliver AI-driven Network insights to NOC engineers, customer support, and senior
   management.
3. Operational Management & Optimization
   Continuous Learning Loops: Establish mechanisms to capture human feedback and
   real-world results to continuously refine AI models and diagnostic rules.
   Performance Monitoring: Own key platform metrics, including diagnostic accuracy,
   false positive rates, and total system latency.
   Scale & Resilience: Manage the operational health of AI services, ensuring high
   availability, graceful degradation during data gaps, and a clear path for future scaling.

Required Core Competencies
Category
Competency Requirement
Artificial
Intelligence
Deep expertise in ML, LLMs (Generative AI), RAG architectures, and
vector search technologies.
Systems
Architecture
Proven ability to design asynchronous, request-response pipelines
and high-performance data ingestion services.
Network Domain
Broad understanding of network telemetry, topology, and the lifecycle
of network incident management is good to have
Operational
Leadership
Experience managing production AI systems with a focus on
auditability and deterministic outcomes.