PRINCIPLE RESPONSIBILITIES:
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.
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.
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.
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.
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 acro

📌 Principal Engineer Bengaluru
🏢 Rakuten Symphony
📍 Bengaluru