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Mid-level GenAI MLOps role in Bangalore with broad toolset attracts many qualified applicants.
ML and MLOps skills transfer across industries, though enterprise identity domain experience moderately increases specificity.
Explicit 5+ years plus mandatory ML/MLOps, cloud, and framework requirements create strict filters.
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Lead end-to-end architecture, development, optimization, and deployment of scalable ML and Generative AI models for real-time, streaming, and batch use cases.
Own and manage MLOps pipelines using tools like Sagemaker and Airflow, enabling cloud-native production ML workloads on AWS using Docker and Kubernetes.
Collaborate cross-functionally with engineers, data scientists, and stakeholders to integrate AI solutions and mentor junior engineers on ML best practices.
Minimum 5 years professional experience in Python development and machine learning.
Strong experience with MLOps practices, tools, and production deployment of ML models.
Familiarity with TensorFlow, PyTorch, llamaindex, langchain, and vector databases.
Experience with AWS cloud services; certifications such as AWS ML Specialty a plus.
Demonstrated ability to lead scalable ML and GenAI projects including model lifecycle management and MLOps pipeline development.
Proficient in deploying cloud-native ML solutions on AWS with containerization (Docker, Kubernetes).
Experienced in handling structured and unstructured data ingestion, embedding models, and building agentic workflows for AI-driven insights.