AI System Architect - Next-Generation Network Management Platform

Location: Remote – open only to candidates based in Bengaluru or Hyderabad, India

Employment Type: Contract (Freelance)

Experience: 10+ years

About Telence Solutions:

Telence Solutions is building a next-generation AI-driven Network Management System designed to support multi-vendor environments and enable intelligent operations, automation, and closed-loop remediation. Our platform aims to combine traditional network management with modern AI capabilities to deliver autonomous and highly scalable network operations.

We are looking for an experienced AI System Architect to help define the architecture, technology roadmap, and AI strategy for this platform.

Role Overview

The AI System Architect will provide technical leadership for the design and evolution of our in-house network management platform. This individual will work closely with software developers, network architects, and product leadership to establish a scalable and future-proof architecture while incorporating industry best practices across AI, machine learning, data engineering, and agentic workflows.

This is a strategic role requiring both architectural depth and hands-on understanding of modern AI technologies.

Key Responsibilities:

AI Platform Architecture

• Define the overall AI architecture for a next-generation network management platform.
• Design scalable frameworks supporting:
• Fault detection and correlation
• Root cause analysis
• Predictive analytics
• Closed-loop automation
• AI-assisted troubleshooting
• Intelligent workflows and agentic AI

AI Models & Technologies

• Evaluate and recommend appropriate AI approaches including:
• Large Language Models (LLMs)
• Small Language Models (SLMs)
• Classical Machine Learning models
• Time-series forecasting models
• Reinforcement Learning
• Retrieval-Augmented Generation (RAG)
• Knowledge Graphs
• Multi-agent systems
• Assess trade-offs between open-source and commercial AI models.
• Define best practices for model training, inference, and lifecycle management.

System Design & Scalability

• Architect microservices-based and cloud-native solutions.
• Define data pipelines and AI workflows.
• Design scalable APIs and integration frameworks.
• Ensure high availability, resiliency, observability, and security.
• Establish AI governance and MLOps practices.

Data Architecture

• Define approaches for handling:
• Streaming telemetry
• Syslogs
• SNMP data
• Configuration data
• Event and alarm information
• Historical performance metrics
• Recommend storage technologies and vector databases.
• Develop data quality and labeling strategies.

Technical Leadership

• Guide engineering teams on architectural decisions.
• Conduct design reviews and technology evaluations.
• Create architecture standards and reference frameworks.
• Mentor developers and AI engineers.
• Stay current with advancements in AI and autonomous networking.

Required Qualifications

• 10+ years of software architecture and platform design experience.
• 5+ years working with AI/ML systems and production deployments.
• Experience with multiple AI frameworks and models.
• Strong understanding of:
• LLMs and Generative AI
• Agentic AI architectures
• RAG systems
• Embedding models
• Vector databases
• MLOps practices
• Prompt engineering and evaluation frameworks
• Experience with Python and AI frameworks such as:
• PyTorch
• TensorFlow
• LangChain
• LlamaIndex
• Hugging Face
• MLflow
• Familiarity with cloud environments including AWS and Azure.
• Experience designing distributed and microservices architectures.
• Strong knowledge of APIs, databases, and event-driven systems.
• Excellent communication and technical leadership skills.

Preferred Experience:

Experience with telecom or network management platforms such as:

• Nokia NSP
• Ciena Navigator
• Cisco Crosswork
• Juniper Paragon
• Blue Planet
• ServiceNow
• Grafana
• Prometheus

Knowledge of:

• IP/MPLS networks
• Optical transport networks
• SD-WAN
• Streaming telemetry
• OpenConfig/YANG
• NETCONF/gNMI
• Kubernetes and containerized environments

What Success Looks Like

Within the first 12 months, you will:

• Define the AI reference architecture for the platform.
• Establish AI model selection and governance processes.
• Guide the development team on scalable AI design patterns.
• Build the foundation for autonomous network operations and closed-loop remediation.
• Help create a world-cl…