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Tier-1 brand plus mid-level experience but niche agentic AI skills keep competition moderate.
Deep LLM, GenAI and Azure data platform requirements limit cross-industry transferability.
Requires explicit agentic AI, Azure/Databricks, LLM and programming skills, making filters stringent.
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Lead execution of TAS Digital Strategy by developing and deploying AI-enabled workflows, technical reporting, and cross-business data insights.
Design, build, and operate agentic AI workflows incorporating data ingestion, diagnostics, reporting, and integration with enterprise data platforms.
Ensure compliance with Responsible AI, data security and governance policies while driving continuous improvement of technical reporting and automation processes.
Engineering degree in Computer Science or related technical field, or any Engineering Degree with Post Graduate Degree/Diploma in Data Sciences.
Minimum 3+ years hands-on experience designing and operating production-grade agentic AI solutions with multiagent workflows and orchestration.
Strong hands-on experience with Azure data platforms (ADF, Databricks/Spark), data lakes, data modelling, and integrating pipelines with enterprise data products.
Proficiency in Python (preferred) and/or Scala, SQL, API/service development, automation, and software engineering practices (Git, CI/CD, unit testing).
Experienced in applying advanced AI techniques (GenAI, LLM, RAG) including prompt engineering, embedding search, output validation, and hallucination mitigation in enterprise settings.
Ability to manage end-to-end AI solution delivery integrating with large-scale enterprise data ecosystems and automate complex workflows with robust orchestrations.
Experience working in asset/plant management or technical asset support environments is a strong advantage.