Data - AI Platform Engineer

Job Title: Data - AI Platform Engineer

Location: Bangalore (Hybrid)

Why should you choose us?

Rakuten Symphony is reimagining telecom, changing supply chain norms and disrupting outmoded thinking that threatens the industrys 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 Japans 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.

Lets 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/.

What Do We Expect From You

We are seeking a Lead Data Engineer to bridge the gap between complex data engineering and advanced AI/ML research. You will move beyond traditional ETL, focusing on building sophisticated data-centric workflows that directly influence model performance. You will be responsible for the data lifecycle - from high-dimensional feature engineering and semantic data structuring to the advanced preprocessing required for LLM training and fine-tuning.

Required Skills & Expertise:

• Education: Bachelor or Master degree in Computer Science, Engineering, Mathematics, or a related field.
• Experience: 8+ years of experience in data engineering and 3+ years experience in leading team of data engineers with a strong focus on building pipelines for ML/AI applications.
• Data-Centric AI: Deep expertise in data preprocessing for LLMs, including cleaning, deduplication, filtering, and synthetic data generation.
• Advanced Data Modelling: Strong proficiency in relational and NoSQL modelling, with specific expertise in managing high-dimensional vector representations and graph-based data structures.
• Python Mastery: Expert-level Python for data manipulation, using high-performance libraries (e.g., Pandas, NumPy, TensorFlow) to handle large-scale data transformations.
• Feature Engineering: Proven experience in designing feature stores that support both real-time inference and batch training for complex ML models.
• Agentic Workflow Orchestration: Familiarity with building agentic workflow using langchain / langgraph preferred.
• Numerical/Mathematical Rigor: Strong understanding of statistical methods, data distribution analysis, and the impact of data quality on model bias and convergence.

Knowledge, Skills, Abilities, & Competencies:

• Data-Model Feedback Loops: Ability to analyse model failure modes and translate them into data-engineering solutions.
• Evaluation & Quality: Experience in designing automated data validation suites and "data-unit testing" to ensure the integrity of datasets before they hit the GPU.
• Mentorship: Ability to lead a team of data engineers in adopting scientific best practices, ensuring code quality and reproducibility in experiments.
• Cross-Functional Collaboration: Ability to work closely with Research Scientists to understand model requirements and translate them into scalable data schemas and ingestion strategies.

What We Look For:

• Comfortable working in global, multi-cultural teams across regions.
• Proven experience of leading teams and delivering business requirements.
• Collaborative and outcome-driven mindset.
• Passion for building domain-specific AI systems with real business impact.
• Ability to balance strategic thinking with hands-on execution.
• Engineers who want to work close to the model, not just consume APIs.
• Strong ownership of model quality and training outcomes.
• Comfort experimenting, measuring, and iterating on models.
• Passion for building domain-specific AI systems with real business impact.
• Ability to balance innovation with production reliability.

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.