Ideal Candidate should have:
20+ years IT Experience - Total
12+ years Data Architecture/Data Engineering
5+ years Telecom Domain
2-5 years GenAI/LLM Implementation
Hands-on Python
Built RAG Solutions
Designed AI Agents
Architected Data Lakes/Lakehouses
Strong Azure/AWS/GCP Experience
Worked on OSS/BSS and 4G/5G Data
Led Enterprise Data & AI Transformation Programs
Job Description ,
We are seeking a Senior AI/ML Data Architect with strong expertise in Large Language
Models (LLMs), AI agents, large-scale data systems, and end-to-end data pipeline creation,
specifically within the Telecom domain. This role is responsible for architecting AI-ready
data platforms that power intelligent automation, advanced analytics, and agent-based
decision systems across OSS/BSS, network operations, and customer engagement.
The ideal candidate will bridge data architecture, AI/ML enablement, and telecom domain
intelligence, enabling scalable, governed, and high-performance AI solutions.
Key Responsibilities,
1. LLM & AI Agent Architecture
Design and implement LLM-enabled architectures, including RAG
(Retrieval-Augmented Generation) solutions using structured and unstructured
telecom data.
Architect and govern AI agents and multi-agent systems for automation,
diagnostics, decision support, and workflow orchestration.
Enable secure integration of LLMs and agents with enterprise data platforms, APIs, and business systems.
Define best practices for prompt engineering, model orchestration, evaluation, and feedback loops.
2. Large-Scale Data Architecture
Lead the design of large-scale, cloud-native data platforms capable of processing high-volume, high-velocity telecom data.
Architect low-latency and batch data ecosystems handling CDRs, network
telemetry, logs, KPIs, customer interactions, and documents.• Select and implement appropriate data architecture patterns such as Lakehouse,
Streaming-first, and Data Mesh.
3. Data Pipelines & Engineering
Design and oversee end-to-end data pipelines covering ingestion, transformation, enrichment, feature creation, and serving layers.
Build AI-ready pipelines optimized for LLM training, inference, agent context retrieval, and model lifecycle management.
Ensure real-time and batch pipeline reliability using observability, data quality
checks, and automated monitoring.
Implement CI/CD-driven pipeline deployments and versioning.
Telecom Domain Enablement
Partner with OSS, BSS, Network Engineering, IT, and Business teams to translate telecom use cases into scalable AI data solutions.
Apply deep understanding of telecom KPIs, network layers, subscriber data, and operational workflows.
Enable AI use cases including:
Network anomaly detection & root-cause analysis
Intelligent NOC and assurance automation
Customer experience analytics & churn prediction
Fraud detection and revenue assurance
Governance, Security & Compliance
Define and enforce data governance, lineage, metadata management, and access control for large data and AI systems.• Ensure compliance with data privacy regulations and secure AI usage across platforms.
Establish responsible AI and LLM governance frameworks.
6. Technical Leadership
Act as a domain expert and solution authority for AI/ML data architecture.
Define architectural standards, reference models, and reusable frameworks.
Mentor engineers, architects, and data teams.
Contribute to enterprise AI and data transformation roadmaps.
Required Skills & Experience
Experience
Overall 20+ years of IT experience
12+ years in data engineering, data architecture, or analytics platforms
5+ years working in the Telecom domain (Network, OSS/BSS, 4G/5G)
Proven experience delivering LLM-based and AI-driven data platforms
Technical Skills
Strong expertise in LLMs, RAG architectures, and enterprise AI integration
Hands-on experience designing AI agents and agent orchestration frameworks
Hands on experience in developing, testing and deployment solution
Fully hands on coding experience in Python or Java
Deep knowledge of large-scale data systems (batch & streaming)
Expertise in creating robust, scalable data pipelines• Strong understanding of ML pipelines, feature engineering, and AI lifecycle needs
Advanced SQL and data modeling skills
Cloud experience with enterprise-scale AI and data workloads
Domain & Soft Skills
Strong telecom data and operations knowledge
Ability to translate complex technical designs into business value
Excellent communication, stakeholder engagement, and leadership skills