Job Description:
Job Title: Architect – AI/ML
Minimum - 8+ years of hands-on experience in software architecture, with at least 5 years specifically focused on designing and implementing enterprise scale AI/ML platforms and ML Ops.
Location: Bangalore (Onsite)
Why should you choose us?
Rakuten Symphony is a Rakuten Group company, that provides global B2B services for the mobile telco industry and enables next-generation, cloud-based, international mobile services. 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!
What Do We Expect From You
As a Principal Architect specializing in AI/ML within Enterprise Architecture, you will address the critical challenge of a fragmented AI framework currently spread across various teams. This role is essential for leading the centralization and robust governance of our inferencing stack, standardizing the model lifecycle, and driving reusability of AI components across all products. Your leadership will build a coherent, scalable, and efficient enterprise AI platform, enabling faster AI development, consistent deployment, and maximized value delivery across the organization.
Responsibilities:
Enterprise AI Framework & Platform Architecture:
- Define and own the architectural blueprint for a centralized, scalable, and governed enterprise AI/ML platform and framework.
- Lead the design of a unified inferencing stack that can serve various AI models efficiently and reliably across different products and use cases.
- Ensure the AI framework supports diverse model types, data modalities, and performance requirements from various business units.
Model Lifecycle Governance & Standardization:
- Establish and enforce best practices and architectural patterns for the end-to-end model lifecycle, from experimentation and training to deployment, monitoring, and retraining.
- Implement governance mechanisms for model versioning, lineage, and approval processes to ensure consistency and compliance.
- Drive the standardization of tools, libraries, and frameworks for AI/ML development and operations across the enterprise.
Reusability & Shared Services:
- Identify common AI/ML patterns and components that can be generalized and offered as reusable services or libraries for product teams.
- Architect shared data features stores, model registries, and inference services to accelerate AI development and reduce redundant efforts.
- Promote inner-sourcing principles for AI/ML assets across the organization.
Performance, Scalability & Cost Optimization:
- Design the AI/ML platform for high performance, low latency inference, and efficient resource utilization, especially for compute-intensive workloads.
- Ensure the architecture is scalable to handle growing volumes of data and increasing numbers of models and inference requests.
- Focus on optimizing the cost of running AI/ML infrastructure and operations.
Roadmap Alignment & Execution
- Translate product roadmap initiatives into detailed architectural plans, prioritizing efforts that enhance convergence and interoperability.
- Work closely with product management and engineering leads to break down large initiatives into manageable architectural increments.
- Anticipate future technical challenges related to cross-domain integration and proactively design solutions.
QUIRED KNOWLEDGE, SKILLS AND EXPERIENCE: ESSENTI
Qualifications:
- 8+ years of hands-on experience in software architecture, with at least 5 years specifically focused on designing and implementing enterprise scale AI/ML platforms and ML Ops.
- Proven track record of centralizing and standardizing fragmented AI/ML frameworks across multiple product teams or business units.
- Deep expertise in building and governing inferencing stacks and managing model lifecycles in production.
- Strong background in architecting scalable, resilient, and cost-effective distributed systems for AI/ML workloads.
Technical Skills
- Expert-level knowledge of major AI/ML frameworks (e.g., TensorFlow, PyTorch) and their deployment patterns.
- Deep understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).
- Strong experience with containerization (Docker) and container orchestration (Kubernetes) for AI/ML model deployment.
- Proficiency with data processing and storage technologies relevant to AI/ML (e.g., Spark, Kafka, data lakes/warehouses).
- Familiarity with at least one major public cloud AI/ML ecosystem (AWS, GCP, or Azure).
- Experience with API design for inference services.
- Proficient in at least one relevant programming language (e.g., Python, Go, Java).
Domain Knowledge
- Familiarity with telecom operational data, including network telemetry, service topology/graphs, and alarm/event data.
Analytical and Problem-Solving Skills
- Exceptional analytical and problem-solving abilities to design complex AI/ML platforms and troubleshoot production issues.
- Strong ability to balance long-term architectural vision with immediate product needs.
Collaboration & Communication
- Excellent written and verbal communication skills, capable of articulating complex AI/ML architectural concepts and strategies to both highly technical and executive audiences.
- Proven ability to influence technical direction and foster collaboration across diverse teams.
Educational Background
- Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field. Master's preferred.
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 conscious of time. Take charge, set clear goals, and engage your team.