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Specialized senior generative AI role at a known industrial brand yields moderate applicant competition.
Requires deep generative AI, production MLOps, and domain expertise, so cross-industry transferability is limited.
Multiple mandatory ML, MLOps and cloud skills plus senior delivery expectations imply high candidate filtering.
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Engineer and deploy production-grade generative AI solutions including large language models (LLMs), visual language models (VLMs), 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.
Build and maintain AI platforms supporting prompt management, embeddings, vector search, retrieval-augmented generation (RAG), and tool-calling workflows with enterprise integration via 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 (beyond research or experimentation).
Strong proficiency in Python and experience with PyTorch, TensorFlow, Hugging Face, and transformer-based architectures.
Experience with AI platform and MLOps tooling including model registries, experiment tracking, CI/CD pipelines, monitoring, cloud-native architectures, and scalable inference patterns (e.g., Kubernetes).
Experienced in full AI lifecycle including model onboarding, fine-tuning, inference optimization, monitoring, and continuous improvement in commercial/industrial contexts.
Skilled in integrating and operating generative AI within enterprise environments collaborating with cloud, data, security, and product teams under governance and responsible AI requirements.
Strong software engineering discipline with delivery focus, capable of owning production systems end-to-end, mentoring engineers, and producing clear technical documentation and operational runbooks.