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Mid-level metro role with common 5–7yr range and broad AI/data skillset.
Role's specialized AI/data platform and cloud tooling moderately limits cross-industry transferability.
Explicit 5–7 years plus mandated Databricks, Azure, MLOps, and infra tooling increases strictness.
Design, build, and deploy scalable AI and ML solutions including multi-agent architectures and LLM integration to optimize data centre operations and intelligent automation.
Develop and maintain robust batch and streaming data pipelines, curated data models, and AI platform foundations using tools like Azure, Databricks, Kafka, and dbt.
Implement end-to-end MLOps and DataOps pipelines with CI/CD, monitoring, infrastructure as code and ensure responsible AI practices, data governance, and security compliance.
5 to 7 years experience in data engineering, AI/ML engineering, or software engineering with production-grade data and AI cloud solutions delivery.
Strong programming skills in Python and proficiency in SQL; experience with JavaScript or Shell scripting is needed.
Hands-on experience with Azure, Databricks, streaming technologies (Kafka, Azure Event Hubs), and tools like Terraform, Docker, Kubernetes for CI/CD and orchestration.
Experience in building AI/ML solutions involving LLMs and agentic systems with tools such as OpenAI, Anthropic, LangChain; knowledge of data governance and Responsible AI integration.
Experienced engineer comfortable working full-stack in a DevOps-oriented environment enabling AI and data platform evolution at scale.
Demonstrated ability to design scalable, secure enterprise AI and data architectures integrating emerging AI technologies and real-time data pipelines.
Proven mentor and collaborator capable of cross-functional engagement, knowledge sharing, and driving operational excellence in production AI and data systems.