Zensar Technologies - Machine Learning Architect - LLM

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 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 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 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 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) & Validation :
• Experience in validating feature data, testing feature functions, and testing ML Store :
• Hands-on experience with feature stores for data governance and Skills :
• Proficiency in relevant programming languages (e.g., Python).
• Strong problem-solving and analytical skills.

(ref : hirist.tech)