Automation / AI Intern for Blade Structural Design Process

What matters most

• Automate risk and compliance workflows

  The role explicitly develops automation for engineering risk assessment and compliance. Prepare to explain how a Python workflow could reduce repetitive work while preserving checks that engineers need to review.

• Strengthen engineering traceability

  The posting calls for links among requirements, risks, controls, and verification activities. Prepare a simple example of how records could be connected and how missing or inconsistent links could be flagged.

• Build usable digital tools

  Dashboards and digital engineering tools using Python, data analytics, and AI are stated duties. Be ready to discuss how a tool would turn assessment data into information engineers can inspect and act on.

• Evaluate AI for engineering knowledge work

  The team wants to evaluate AI and large language models for knowledge management and workflow automation. Prepare to distinguish a useful prototype from a dependable engineering workflow, including how outputs would be checked.

Likely challenges to think through

• Learning the engineering context before automating it

  The posting offers training on large-scale blade structural risk assessment templates. A likely challenge is moving quickly without misreading the meaning of template fields; prepare to describe how you would clarify terms with engineers and validate a small sample before scaling.

• Keeping linked records reliable

  Connecting requirements, risks, controls, and verification can make gaps visible, but changes to one record may leave links outdated. As a preparation hypothesis, consider how stable identifiers, change checks, and exception reports could balance traceability with maintenance effort.

• Balancing AI speed with reviewability

  LLMs may help organize engineering knowledge, but a plausible risk is presenting an unsupported answer as fact. Prepare to explain how you would test outputs against source material and keep human review in the workflow.

Topics to prepare

• Blade structural risk assessment context

  Review how a structural-design concern might be recorded, assessed, controlled, and checked. Sketch a hypothetical risk-assessment template and mark where missing information could affect a decision. The posting centers on blade structural design and says the internship includes training on large-scale structural risk-assessment templates; no specific assessment method is stated.

• Compliance and requirements traceability

  Draw a small example linking three requirements to risks, controls, and verification activities; identify any broken links. This directly reflects the advertised compliance workflows and traceability work. Treat the example as preparation, not as Siemens Gamesa's process.

• AI and LLM suitability for engineering knowledge

  Compare a conventional searchable document set with a hypothetical LLM-assisted question-answering workflow. Note how you would check answer accuracy and preserve links to source material. The role calls for evaluating AI and LLM applications for knowledge management and workflow automation, not deploying a specified model.

Skills to revise

• Python workflow automation

  Using a small synthetic table of requirements, risks, and controls, write Python code to flag missing identifiers and unmatched references, then produce a summary. Strong Python programming is explicitly requested, and the work includes automating engineering risk and compliance workflows.

• Spreadsheet data preparation

  Create a sample risk register in Excel and automate a repeatable check for blank fields, duplicate IDs, and inconsistent entries. Excel automation is one of several experience areas named in the posting; this is a suggested way to practise handling template-like data.

• Dashboard development

  Build a compact dashboard from synthetic workflow data showing counts of open risks and missing verification links, and explain what each measure does and does not show. Dashboard creation is an explicit responsibility; Power BI is named as a possible experience area, not a required platform.

• Small-scale AI evaluation

  Test an AI-assisted summary or retrieval approach on a few invented engineering notes. Record the question, output, source evidence, and any unsupported claims. This practises the posting's stated AI/LLM evaluation work without assuming a particular tool or company dataset.

• Prepare a Python project walkthrough

  The role calls for strong Python skills and engineering automation. Select a real project you can explain, and prepare a short walkthrough of its inputs, code structure, tests, output, and limitations.

• Sketch a risk-assessment automation exercise

  The posting mentions engineering risk assessment and compliance workflows. As a practice exercise, use fictional data to show how a script could flag missing fields or inconsistent entries; explain what would still need engineering review.

• Design a traceability example

  Prepare a small sample linking requirements, risks, controls, and verification activities—the relationships named in the posting. Show how you would find an unlinked item or track a change.

• Build a simple dashboard mock-up

  The team creates dashboards and digital engineering tools. Using sample data, prepare a Python or Power BI view of risk or verification status, and be ready to explain the measures and data-quality checks you chose.

• Outline an AI evaluation

  The role evaluates AI and large language models for engineering knowledge management and workflow automation. Propose a limited use case and a way to test accuracy, source traceability, and when human review is needed; present it as an idea, not an existing team project.

• Prepare evidence and questions for the interview

  Gather concise examples of your Python work and any relevant Excel automation, Power BI, MATLAB, database, or AI work you have done. Ask which risk-assessment templates and traceability challenges the intern would work with, and how the stated structured training supports that work.