PRINCIPLE RESPONSIBILITIES:

1. AI/ML Platform Development

- Implement and maintain components of the centralized enterprise AI/ML platform, including model serving, inference APIs, and shared ML libraries.
- Build and operate robust, production-grade ML pipelines covering data ingestion, feature engineering, model training, deployment, and inference.
- Contribute to the development of shared feature stores, model registries, and inference services used by multiple product teams.

2. MLOps & Model Lifecycle

- Develop and maintain CI/CD pipelines for ML model training, evaluation, and deployment.
- Implement automated model monitoring, drift detection, and alerting mechanisms.
- Build tooling to support model versioning, lineage tracking, and rollback capabilities.

3. Performance & Reliability Engineering

- Optimize inference pipelines for latency, throughput, and resource efficiency.
- Contribute to scalability improvements to handle growing model counts and inference request volumes.
- Investigate and resolve production issues in the AI/ML platform.

4. Reusability & Inner-Sourcing

- Develop well-documented, reusable ML components and libraries for consumption by product

engineering teams.

- Actively participate in and contribute to the inner-source AI/ML asset ecosystem.
- Write technical documentation, runbooks, and integration guides for shared services.

5. Collaboration & Engineering Excellence

- Work closely with data scientists, ML engineers, and product architects to translate model requirements

into production implementations.

- Participate in architectural and code reviews, upholding high standards for quality, security, and

maintainability.

- Mentor junior engineers on AI/ML engineering best practices.

REQUIRED KNOWLEDGE, SKILLS AND EXPERIENCE:

Experience and Expertise

- 5+ years of software engineering experience,



with at least 3 years focused on building and operating AI/ML systems in production.
- Demonstrated experience building and maintaining ML pipelines and model serving infrastructure at scale.
- Hands-on experience with MLOps practices including CI/CD for ML, model monitoring, and lifecycle management.
- Proven ability to build reliable, maintainable software components used by multiple teams.

Technical Skills

- Strong proficiency in Python; familiarity with Go or Java is a plus.
- Hands-on experience with major AI/ML frameworks: TensorFlow and/or PyTorch.
- Practical knowledge of MLOps tools: MLflow, Kubeflow, or equivalent (Vertex AI, SageMaker, Azure ML).
- Solid experience with containerization (Docker) and orchestration (Kubernetes) for ML workloads.
- Experience with data processing technologies (Spark, Kafka) and data storage (data lakes, feature stores, data warehouses).
- Familiarity with at least one public cloud AI/ML ecosystem (AWS, GCP, or Azure).
- Experience designing and consuming RESTful or gRPC APIs for inference services.

Analytical and Problem-Solving Skills

- Strong debugging and root-cause analysis skills for distributed AI/ML systems.
- Ability to profile and optimize ML inference for latency and cost.

Collaboration & Communication

- Clear written and verbal communication; ability to document technical systems for a broad audience.
- Team-oriented working style with data scientists, engineers, and architects.

Educational Background

- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.

Additional Skills

- Experience in the telecom domain or with OSS (Operations Support Systems) data and use cases.
- Familiarity with GPU/TPU-based inference optimization.
- Knowledge of responsible AI practices (fairness, explainability, privacy).
- Contributions to open-source MLOps or AI infrastructure projects.
- Experience with streaming/real-time inference architectures.

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