Principal Engineer AI/ML

Job Title: Principal Engineer – AI ML

Location: Bangalore, India

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 approximately 1.9 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/.

JOB PURPOSE:

As a Principal Engineer in the AIML Framework team within Enterprise Architecture, you will be the senior

technical authority driving the implementation of Rakuten Symphony's centralized enterprise AI/ML

platform. You will own end to-end design and delivery of critical platform subsystems — the unified

inferencing stack, model lifecycle tooling, and shared AI services — and set the engineering standards that

product teams across the organization depend on. You bridge the gap between architectural vision and

production reality, ensuring the platform is robust, scalable, cost efficient, and genuinely useful to the

teams it serves.

PRINCIPLE RESPONSIBILITIES:

1. Technical Ownership of Core Platform Subsystems

• Own the design and implementation of major subsystems of the enterprise AI/ML platform including the

centralized inferencing stack, model registry, feature store integrations, and MLOps tooling.

• Drive technical decisions for platform components, evaluating build vs. buy vs. open-source trade-offs

with a long-term enterprise lens.

• Ensure platform components meet strict reliability, performance, security, and cost targets.

2. MLOps & Model Lifecycle Engineering

• Lead the engineering of enterprise-grade CI/CD pipelines for ML, covering training, evaluation,

deployment, canary rollouts, and automated rollback.

• Design and implement comprehensive model observability: performance monitoring, data drift

detection, bias tracking, and alerting.

• Own the tooling for model versioning, lineage, and governance compliance across the enterprise.

3. Platform Architecture & Engineering Standards

• Translate the Principal Architect's architectural blueprints into detailed technical designs and engineering

specifications.

• Define and enforce engineering standards, coding patterns, and API contracts for all platform

components.

• Conduct design and code reviews across the team, raising the overall quality bar.

4. Performance, Scalability & Cost Engineering

• Lead performance engineering for the inferencing stack: profiling, bottleneck identification, latency

optimization, and throughput scaling.

• Architect for cost efficiency in compute-intensive AI/ML workloads, including GPU/TPU utilization

strategies.

• Design for scale: thousands of concurrent inference requests, hundreds of models, petabyte-scale data

volumes.

5. Reusability, Inner-Sourcing & Developer Experience

• Own the shared AI/ML services and libraries strategy, ensuring platform APIs are intuitive and well

documented for product teams.

• Drive inner-sourcing initiatives so AI/ML assets are discoverable, reusable, and governed across business

units.

• Act as the primary technical point of contact for product engineering teams onboarding to the platform.

6. Technical Leadership & Mentorship

• Provide senior technical mentorship to engineers across the AIML Framework team and product AI/ML

teams.

• Represent the engineering perspective in architectural reviews and cross-functional technical

discussions.

• Identify and incubate emerging technologies relevant to the enterprise AI platform roadmap.

Experience and Expertise

• 8+ years of software engineering experience, with at least 5 years focused on

designing and building enterprise-scale AI/ML platforms and MLOps systems in

production.

• Proven ownership of major AI/ML platform subsystems used by multiple teams inferencing services,

model registries, feature stores, or equivalent.

• Demonstrated track record of centralizing or standardizing fragmented AI/ML

tooling across product teams.

• Deep experience with production model lifecycle management: deployment,

monitoring, drift detection, retraining, and retirement.

• Track record of influencing engineering direction and raising engineering quality

across teams.

Technical Skills

• Expert-level Python; proficiency in Go or Java strongly preferred.

• Deep hands-on expertise with TensorFlow and/or PyTorch, including

deployment and optimization patterns.

• Expert knowledge of MLOps tools: MLflow, Kubeflow, and at least one cloud

native equivalent (Vertex AI, SageMaker, Azure ML).

• Expert-level containerization (Docker) and Kubernetes — including custom

operators, resource management, and GPU scheduling for ML workloads.

• Strong data engineering skills: Spark, Kafka, data lakes, warehouses, and feature

stores.

• Deep familiarity with at least one public cloud AI/ML ecosystem (AWS, GCP, or

Azure) at an architectural level.

• API design expertise for high-availability, low-latency inference services (REST

and gRPC).

• Experience with model optimization techniques: quantization, distillation,

ONNX, TensorRT, or equivalent.

Analytical and Problem-Solving Skills

• Exceptional ability to diagnose complex production issues in distributed AI/ML

systems.

• Strong capacity to balance long-term platform vision against immediate team

delivery needs.

• Ability to evaluate competing technical approaches with clear, evidence-based

reasoning.

Collaboration & Communication

• Excellent written and verbal communication; able to present complex

engineering decisions to architects, product managers, and senior leadership.

• Proven ability to influence technical direction and align diverse engineering

teams without direct authority.

• Strong cross-functional collaboration skills with data scientists, MLOps

engineers, cloud architects, and security teams.

Educational Background

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine

Learning, or related technical field. Master's preferred.

Additional Skills

• Experience in the telecom domain or with OSS (Operations Support Systems)

data and AI/ML use cases.

• Hands-on experience with GPU/TPU inference optimization (CUDA, TensorRT,

JAX).

• Knowledge of responsible AI practices: fairness, interpretability, privacy

preserving ML.

• Contributions to open-source MLOps, model serving, or AI infrastructure

projects (e.g., KServe, Ray Serve, Triton Inference Server).

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.