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 explicit 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.

📌 Artificial Intelligence Expert (Bengaluru)
🏢 Atria Convergence Technologies (ACT)
📍 Bengaluru