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High due to Tier-1 brand, remote-friendly role, mid-level generalist ML expectations, and popular AI engineer title.
Medium because core ML/LLM engineering skills transfer, but enterprise deployment and field-facing experience increase domain specificity.
High due to explicit 4+ years, mandatory LLM/ML production skills, and cloud/Kubernetes deployment requirements.
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Design and deliver end-to-end AI solutions including agentic AI systems, LLM applications, and predictive ML pipelines for complex enterprise problems in APAC.
Own full development lifecycle: problem framing, model development, API integration, production deployment, and MLOps/LLMOps for continuous improvement.
Engage directly with customers and internal experts to build and deploy production-grade AI applications with measurable business impact.
4+ years of hands-on AI/ML engineering experience with end-to-end model development and deployment.
Proven experience building LLM-powered applications (e.g., RAG, fine-tuning, agentic workflows).
Strong Python skills and familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent).
Experience deploying AI models in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes).
Experienced in rapidly designing and shipping multiple predictive ML models involving classification, regression, time-series forecasting, or anomaly detection using structured/tabular data.
Comfortable working autonomously within a customer-facing field engineering environment collaborating with cross-functional teams including Kaggle Grandmasters.
Strong background integrating AI models into scalable backend services and enterprise infrastructures with emphasis on production reliability and responsible AI controls.