AI/ML Data Architect (Telecom)

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