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Tier-1 employer, metro location, and mid-level experience increase applicant density despite niche agentic AI requirements.
Highly specialized GenAI, LLM, and Azure data platform requirements substantially limit cross-industry transferability.
Explicit 3+ years and mandatory agentic AI, Azure, Databricks, and LLM skills indicate high selection rigidity.
Lead design and delivery of AI-enabled workflows and automation to support Shell’s Technical Asset Support (TAS) digital strategy.
Build and orchestrate agentic AI solutions integrated with enterprise data platforms for autonomous management information and analytics.
Drive continuous improvement by simplifying and automating technical reporting, ensuring compliance with AI and data governance standards.
Engineering degree in Computer Science, related technical field, or Engineering degree plus postgraduate qualification in Data Sciences.
Minimum 3+ years hands-on experience designing, building, and operating production-grade agentic AI solutions including orchestration and guardrails.
Strong hands-on experience with Azure data platforms (ADF, Databricks/Spark), data lakes, data modelling, and pipeline engineering.
Proven expertise in GenAI, LLMs, retrieval-augmented generation, prompt management, and output validation with hallucination mitigation.
Experienced in deploying multi-agent AI workflows with robust execution logic and cloud integration in enterprise environments.
Skilled in Python, Scala, SQL, API/service development, automation, and software engineering best practices (Git, CI/CD, testing).
Familiarity with asset/plant management domain or prior experience supporting asset operations is advantageous.