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Zensar Technologies - Machine Learning Architect - LLM Job Opening In Bengaluru – Now Hiring Zensar Technologies


Job description

<p><p><b>Role :</b> MLOps / ML Architect<br/><br/><b>Experience :</b> 14-16 years total, with 3-5 years of relevant experience on Sagemaker AI.<br/><br/><b>Location :</b> Bangalore<br/><br/><b>Work Mode :</b> Hybrid<br/><br/><b>Notice Period :</b> Immediate to 20 Days</p><p><b><br/></b></p><p><b>Job Summary :</b></p><p><br/></p><p>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.

</p><p><br/></p><p>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 :</b></p><p><br/></p><p>- Structure and build modular ML pipelines (batch, real-time, and LLM) that can be independently developed, tested, and operated.<br/><br/></p><p>- Design and implement MLOps principles of automated testing, versioning, and monitoring for features and models.<br/><br/></p><p>- Govern data within a feature store and establish practices to promote collaboration and consistency between offline training and online operations.<br/><br/></p><p>- Deploy real-time models that are connected to a feature store, ensuring low-latency performance.<br/><br/></p><p>- Log and monitor features and models using a feature store to maintain system health.<br/><br/></p><p>- Schedule and manage feature pipelines and batch inference pipelines.<br/><br/></p><p>- Develop and maintain user interfaces for ML Skills :</b></p><p><b><br/></b></p><p><b>- Experience :</b> 14 to 16 years of total experience, with 3-5 years of relevant, hands-on experience in Sagemaker AI.</p><p><b><br/></b></p><p><b>- MLOps Expertise :</b> Strong understanding of MLOps principles, including CI/CD for machine learning, automated testing, and versioning of models and Skills :</b></p><p><br/></p><p>- Proven ability to train ML models from time-series tabular data.<br/><br/></p><p>- Experience in personalizing LLMs using fine-tuning and Retrieval-Augmented Generation (RAG).<br/><br/></p><p>- Ability to validate models using evaluation data from a feature Engineering :</b></p><p><br/></p><p>- Expertise in identifying and developing reusable, model-independent features.<br/><br/></p><p>- Experience in identifying and developing model-dependent features.<br/><br/></p><p>- Ability to identify and develop on-demand (real-time) & Validation :</b></p><p><br/></p><p>- Experience in validating feature data, testing feature functions, and testing ML Store :</b></p><p><br/></p><p>- Hands-on experience with feature stores for data governance and Skills :</b></p><p><br/></p><p>- Proficiency in relevant programming languages (e.g., Python).<br/><br/></p><p>- Strong problem-solving and analytical skills.</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations


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