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Medium — mid-level, metro role with broad data/AI skillset and hyperscaler-backed employer increases applicant density.
High — role requires specialised ML, MLOps, Databricks, Azure, and streaming platform experience, limiting cross-industry transferability.
High — explicit 5–7 years plus many mandatory cloud, Databricks, MLOps, streaming, and infra tool requirements.
Design, build, and deploy scalable AI/ML and data engineering solutions to enhance operational efficiency and intelligent decision-making in hyperscale data centres.
Develop and maintain robust batch and streaming data pipelines, curated data models, and AI integrations using modern cloud platforms like Azure and Databricks.
Implement end-to-end MLOps and DataOps pipelines, ensuring observability, reliability, and compliance in AI and data systems.
5 to 7 years of experience in data engineering, AI/ML engineering, or software engineering with delivery of production-grade data and AI solutions in cloud environments.
Strong hands-on experience with Azure, Databricks, Python, SQL, and data pipeline tools including dbt, Kafka, and Azure Event Hubs.
Proficiency with AI/ML frameworks and platforms such as OpenAI, Anthropic, LangChain, including building agentic AI and LLM-based applications.
Experience with MLOps/DataOps practices including CI/CD, Docker, Kubernetes, Terraform, and implementing data governance and Responsible AI practices.
Engineer fluent at full-stack AI and data engineering with a DevOps-oriented, production-grade operational focus.
Experienced in architecting scalable, secure, and reusable AI and data platform components integrating emerging LLM and generative AI technologies.
Collaborative and capable of mentoring junior engineers and contributing to cross-functional AI/Data platform evolution and governance.