Staff Engineer, AI/ML
Rakuten Symphony (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 35–65 LPA
Posted on 9 Sept
Reach people at Rakuten Symphony
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Rakuten Symphony you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop and productionize AI/ML models for anomaly detection, fault prediction, and root cause analysis across telecom network data.
- Build and maintain the AI harness infrastructure including training, evaluation, inference, monitoring, and lifecycle management to deploy models from development to production.
- Partner with data engineering teams to define data contracts and ensure robust, scalable pipelines for real-time AI-driven network operations.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, Statistics or related field (or equivalent experience).
- Minimum 8 years of experience in ML/AI engineering with production deployment on live high-volume datasets.
- Proven experience with time-series ML models including anomaly detection, forecasting, or classification using deep learning and classical approaches.
- Strong Python engineering skills and experience with SQL databases (ClickHouse, YugabyteDB, or PostgreSQL); solid software engineering practices including CI/CD and containerization.
Ideal Candidate Profile
- Experienced in telecom OSS, network operations, or AIOps with a focus on operational AI model impact in production environments.
- Skilled in building end-to-end ML infrastructure: from data handling and training to serving, monitoring, and retraining models reliably in critical applications.
- Able to work with advanced AI technologies including LLMs, streaming data platforms, and rigorous evaluation methods like shadow deployments and human-in-the-loop feedback.