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.

REQUIRED KNOWLEDGE, SKILLS AND EXPERIENCE:

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.
- Robust 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).
- Experience with streaming/real-time inference and event-driven ML architectures.
- Familiarity with FinOps principles applied to AI/ML infrastructure.

📌 Principal Engineer - AI ML (Bengaluru)
🏢 Rakuten Symphony
📍 Bengaluru