Padmi

Engineer Mlops / Llmops 7+ Years Noida/gurugram Hybrid (India)

Delhi NCRPosted 1 month ago
Software engineeringSeniorFull Time; Regular
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Engineer - MLOps / LLMOps - 7+ Years - Noida/Gurugram (Hybrid) Are you a seasoned professional with 7+ years of expertise in MLOps and LLMOps Are you passionate about developing robust machine learning pipelines and operational frameworks If yes, we are looking for someone like you to join our dynamic team In this hybrid role based out of Noida/Gurugram, you'll be empowered to lead projects, innovate, and redefine operational excellence in the ever-evolving world of machine learning and analytics. Location: Noida/Gurugram (Hybrid) Your Future Employer: A leading name in Technology & Analytics practices that fosters an inclusive work setting, values diversity, and champions the creative and analytical minds shaping modern industries. Join a team where innovation meets integrity, and impact is built through collaboration. Responsibilities Design and implement robust frameworks for machine learning operations, from model deployment to monitoring and updating models in production environments. Collaborate with data scientists, developers, and engineers to automate workflows while optimizing performance and scalability. Effectively govern, version, and monitor ML and LLM models across distributed systems. Leverage cloud services and integrate across datasets to deliver scalable, secure, and productive infrastructure. Influence technology decisions, recommend best practices, and contribute to open-source projects related to MLOps and LLMOps. Requirements A minimum of 3 years of experience in MLOps, LLMOps, or related areas with hands-on expertise in industry-leading tools and frameworks such as Kubeflow, MLFlow, or TensorFlow Extended (TFX). Solid expertise in coding languages like Python, R, or similar, with a demonstrated ability to build and deploy ML pipelines. Proficiency in cloud platforms such as AWS, GCP, or Azure, including services like S3, Lambda, or Kubernetes. Experience in monitoring machine learning models post-deploy .

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