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Strong Tier-1 brand, metro location, and mid-level nature increase applicant competition despite niche agentic AI specialization.
Specialized agentic AI and energy asset context moderately reduce cross-industry transferability.
Mandatory 3+ years agentic AI, LLM/RAG, Azure data platform and production experience increases screening strictness.
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Lead design and implementation of AI-enabled workflows and agentic AI solutions to automate TAS-specific technical reporting and analytics.
Integrate AI agents with enterprise data platforms (AIF, DAP, SDU, AMDP) to enable autonomous management information and DIY analytics.
Ensure compliance with Responsible AI, data security, and Shell governance while driving continuous improvement in technical reporting landscape.
Engineering degree in Computer Science or related technical field, or any Engineering Degree plus Post Graduate Degree/Diploma in Data Sciences.
Minimum 3+ years of hands-on experience designing, building, and operating production-grade agentic AI solutions including multiagent workflows, orchestration, logging, and retries.
Strong hands-on experience with Azure data platforms (ADF, Databricks/Spark), data lakes, data modelling, and building monitored data pipelines integrated with enterprise data products.
Proven expertise in GenAI & LLM capabilities including RAG patterns, embeddings, prompt management, output evaluation, and hallucination mitigation.
Experienced in delivering AI-driven digital transformation aligned to asset management or industrial workflows in a fast-paced, governance-focused environment.
Strong software engineering skills with Python and API development, comfortable with automation, agile methodologies, and continuous integration/deployment.
Capable of managing complex end-to-end technical solutions with resilience, prioritization, and collaborative cross-functional stakeholder engagement.