Senior Architect, Autonomous Networks & AI

Job Description:

Why should you choose us?

Rakuten Symphony is reimagining telecom, changing supply chain norms and disrupting outmoded thinking that threatens the industry’s pursuit of rapid innovation and growth. Based on proven modern infrastructure practices, its open interface platforms make it possible to launch and operate advanced mobile services in a fraction of the time and cost of conventional approaches, with no compromise to network quality or security. Rakuten Symphony has operations in Japan, the United States, Singapore, India, South Korea, Europe, and the Middle East Africa region. For more information, visit: https://symphony.rakuten.com

Building on the technology Rakuten used to launch Japan’s newest mobile network, we are taking our mobile offering global.

To support our ambitions to provide an innovative cloud-native telco platform for our customers, Rakuten Symphony is looking to recruit and develop top talent from around the globe. We are looking for individuals to join our team across all functional areas of our business – from sales to engineering, support functions to product development.

Let’s build the future of mobile telecommunications together!

About Rakuten Group, Inc. (TSE: 4755) is a global leader in internet services that empower individuals, communities, businesses and society. Founded in Tokyo in 1997 as an online marketplace, Rakuten has expanded to offer services in e-commerce, fintech, digital content and communications to 2 billion members around the world. The Rakuten Group has over 30,000 employees, and operations in 30 countries and regions. For more information visit https://global.rakuten.com/corp/.

About the Role

We are executing an Autonomous Networks Level 4+ (TM Forum AN-4.0) transformation, moving network operations from human-driven to AI-driven closed loops. We are seeking an experienced Autonomous Networks Architect to own the High-Level and Low-Level Design (HLD/LLD) for a solution stack spanning SMO (Service Management & Orchestration), RIC (non-RT), AI/ML models, LLMs, AI Agents, and Agentic AI workflows .

You will architect the intelligence layer of the network, intent-based automation, closed-loop control, and AI agents that sense, decide, and act across RAN and network operations and drive the design from concept through vendor implementation to production rollout.

Key Responsibilities

Own HLD/LLD for the AN stack : Author and maintain architecture and detailed design documents across SMO, RIC, AI/ML platforms, LLM services, and agent frameworks, baseline with stakeholders (Network Engineering, Operations, Data/AI teams, Security, Vendors)

SMO architecture : O-RAN Service Management and Orchestration design O1, O2, A1 interfaces, lifecycle management of network functions and RIC platforms, integration with NFV orchestration and OSS/BSS

Closed-loop automation design : define sense->decide->act loops with clear ownership boundaries across SMO-non-RT, RIC-network, intent-based interfaces, policy hierarchies, and escalation/delegation to humans (L3->L4 autonomy progression)

AI/ML platform design : MLOps pipeline for RAN and network operations use cases (anomaly detection, capacity forecasting, energy saving, traffic steering, anomaly-based alarm correlation), feature stores, model registry, training/serving infrastructure, drift monitoring, and model governance

LLM & GenAI architecture : design RAG-based network knowledge assistants, LLM integration for ticket/alarm summarization, config generation, natural-language intent translation, model selection (open-weights vs. API), fine-tuning strategy, prompt/context engineering, and cost/latency engineering

AI Agent & Agentic AI design : architect multi-agent frameworks tool use, planning, memory, multi-agent orchestration (e.g., LangGraph/CrewAI-class frameworks), guardrails, human-in-the-loop approval flows, and auditability of autonomous actions, define agent action scopes with rollback and blast-radius containment

Network data architecture : telemetry ingestion (streaming, gNMI), data lakehouse design for training/inference, network digital twin concepts for safe agent testing

Responsible AI & governance : explainability, bias/quality gates, autonomous-action policy enforcement, safety interlocks, and compliance alignment (TM Forum AI ethics, local regulation)

Standards & frameworks alignment : TM Forum Autonomous Networks (AN levels, IG1267/AI Analytics), O-RAN Alliance (WG2/WG3), 3GPP SA5 intent management

Drive vendor/OEM technical evaluation , review vendor HLD/LLD deliverables, and own design authority across multi-vendor integration

Guide implementation teams , resolve design ambiguities, define test strategy for AI components (offline eval, shadow mode, A/B, canary autonomy), and support production cutover and operations transition

Mentor engineers and establish AI design review forums, patterns, and documentation standards

Required Qualifications

12+ years in telecom (RAN/OSS/network software), with 5+ years as solution/technical architect owning HLD/LLD for at least one large automation/orchestration program

O-RAN architecture depth : SMO, non-RT, E2/O1/A1 interfaces, hands-on experience with at least one RIC ecosystem, rApp development or platform architecture

AI/ML engineering credibility : designing production ML systems, model lifecycle, MLOps tooling (MLflow/Kubeflow/SageMaker-class), streaming data pipelines, ability to architect and defend training/serving designs for network use cases

LLM/GenAI hands-on expertise : RAG architectures, vector databases, prompt engineering, LLM APIs and open-weight deployment, fine-tuning trade-offs, evaluation harnesses (accuracy, hallucination control, latency, cost)

Agentic AI framework experience : multi-agent orchestration, tool/function calling, guardrails, human-in-the-loop patterns with pragmatic judgment on where agents are appropriate vs. deterministic automation

Closed-loop & intent-based automation : designing autonomy loops with escalation paths, safety interlocks, and measurable autonomy-level progression (TM Forum AN levels 1–5)

Strong RAN domain knowledge (4G/5G, O-RAN RAN architecture, NTN familiarity a plus), plus cloud-native engineering (Kubernetes, microservices, CI/CD, observability)

Protocols & integration: REST/gRPC, gNMI/streaming telemetry, Kafka-class event buses, NETCONF/YANG familiarity

Proven ability to produce and defend architecture artifacts: architecture diagrams, sequence/interface specs, NFRs (latency budgets for control loops, availability, scaling), and AI-specific NFRs (model accuracy SLOs, fallback behavior)

Excellent written and verbal communication experience presenting AI/autonomy strategy to senior leadership and customers

Preferred Qualifications

Direct experience on a TM Forum AN Level 3->4 transformation program, or catalyst/PoC in autonomous networks

Experience deploying LLM-based copilots/agents in production network operations (NOC assistant, alarm triage agents, autonomous ticket resolution)

Familiarity with network digital twins, simulation environments for agent safety testing

Education

B.E./B.Tech/M.Tech in Electronics & Communications, Computer Science, AI/ML, or equivalent

Rakuten SHUGI Principles:

Our worldwide practices describe specific behaviours that make Rakuten unique and united across the world. We expect Rakuten employees to model these 5 SHUGI Principles of Success.

Always improve, always advance. Only be satisfied with complete success - Kaizen.

Be passionately professional. Take an uncompromising approach to your work and be determined to be the best.

Hypothesize - Practice - Validate - Shikumika. Use the Rakuten Cycle to success in unknown territory.

Maximize Customer Satisfaction. The greatest satisfaction for workers in a service industry is to see their customers smile.

Speed!! Speed!! Speed!! Always be conscio