Role : MLOps / ML Architect

Experience : 14-16 years total, with 3-5 years of relevant experience on Sagemaker AI.

Location : Bangalore

Work Mode : Hybrid

Notice Period : Immediate to 20 Days

Job Summary :

As an MLOps / ML Architect, you will be a key leader in the design, development, and operation of scalable and resilient machine learning systems. With a strong focus on MLOps principles, you will be responsible for building modular ML pipelines for batch, real-time, and LLM applications.

Your expertise will be crucial in leveraging Sagemaker AI and a feature store to ensure data consistency, automate testing, and monitor models. This role requires a hands-on architect who can drive best practices and deliver robust, production-ready ML solutions.

Key Responsibilities :

- Structure and build modular ML pipelines (batch, real-time, and LLM) that can be independently developed, tested, and operated.

- Design and implement MLOps principles of automated testing, versioning, and monitoring for features and models.

- Govern data within a feature store and establish practices to promote collaboration and consistency between offline training and online operations.

- Deploy real-time models that are connected to a feature store, ensuring low-latency performance.

- Log and monitor features and models using a feature store to maintain system health.

- Schedule and manage feature pipelines and batch inference pipelines.

- Develop and maintain user interfaces for ML systems.

Required Skills :

- Experience : 14 to 16 years of total experience, with 3-5 years of relevant, hands-on experience in Sagemaker AI.

- MLOps Expertise : Strong understanding of MLOps principles, including CI/CD for machine learning, automated testing, and versioning of models and features.

Modeling Skills :

- Proven ability to train ML models from time-series tabular data.

- Experience in personalizing LLMs using fine-tuning and Retrieval-Augmented Generation (RAG).

- Ability to validate models using evaluation data from a feature store.

Feature Engineering :

- Expertise in identifying and developing reusable, model-independent features.

- Experience in identifying and developing model-dependent features.

- Ability to identify and develop on-demand (real-time) features.

Testing & Validation :

- Experience in validating feature data, testing feature functions, and testing ML pipelines.

Data Store :

- Hands-on experience with feature stores for data governance and collaboration.