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Tier-1 brand plus senior generative AI role with broad MLOps skills increases competition.
Specialized generative AI and MLOps skills transfer across industries but prefer enterprise/industrial experience.
Requires advanced degrees, proven production LLM delivery, and specific MLOps/tooling expertise.
Engineer and deploy production-grade generative AI solutions including LLMs, VLMs, and multimodal models with a focus on inference, scalability, and reliability.
Design and operate LLM Ops pipelines covering model lifecycle management including fine-tuning, evaluation, deployment, rollback, and continuous improvement.
Build and maintain AI platforms and services supporting prompt management, embeddings, vector search, RAG, and integrate AI capabilities into enterprise applications using APIs and microservices.
Master’s degree in Computer Science, AI, Machine Learning, or related field, or equivalent hands-on industry experience.
Proven experience deploying and operating generative AI models in production environments (not research or experimentation).
Strong proficiency in Python and experience with PyTorch, TensorFlow, Hugging Face, and transformer-based architectures.
Experience with MLOps tools (model registries, experiment tracking, CI/CD pipelines), cloud-native architectures, and scalable inference (e.g., Kubernetes deployments).
Experienced in end-to-end generative AI model lifecycle management and operationalization in enterprise or industrial contexts.
Comfortable owning production systems with a delivery-focused mindset emphasizing reliability, observability, and continuous improvement.
Skilled at cross-functional collaboration with cloud, data, security, and product teams to meet enterprise standards and drive business value.