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High candidate competition due to strong brand, popular generative AI role, metro hiring, and broad skill requirements.
Medium because generative AI and MLOps skills transfer widely, though enterprise energy domain adds moderate constraints.
High due to lead-level seniority, mandatory production generative AI experience, and specific MLOps and LLM tech requirements.
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Own end-to-end engineering and deployment of production-ready generative AI solutions including large language, vision, and multimodal models focusing on inference scalability and reliability.
Design and operate LLM Ops pipelines covering model versioning, fine-tuning, deployment, rollback, and lifecycle management while integrating AI into enterprise applications via APIs and microservices.
Implement and maintain AI platforms and MLOps best practices such as CI/CD, automated testing, monitoring, and collaborate cross-functionally to meet enterprise security and governance standards.
Master’s degree in Computer Science, AI, Machine Learning or related field; PhD is a plus but not mandatory.
Proven experience deploying and operating generative AI models in production environments.
Strong proficiency in Python with experience in PyTorch, TensorFlow, Hugging Face, and transformer architectures.
Experience with AI platform tooling and MLOps practices including CI/CD pipelines, cloud-native architectures, containers, and scalable inference patterns.
Experienced in delivering secure, scalable industrial AI applications integrated across enterprise systems via modern APIs and microservices.
Strong software engineering discipline with demonstrated ability to own, operate, and improve production AI systems end-to-end.
Familiar with advanced generative AI techniques including RAG systems, vector search, prompt engineering and lifecycle management in cloud environments.