Automation / AI Intern for Blade Structural Design Process

Round 1: Technical interview covering Python, AI, and automation concepts → Round 2: Technical interview focused on engineering workflow and data analytics → Round 3: HR interview assessing cultural fit and motivation

Siemens Energy fosters a culture of innovation, collaboration, and continuous learning. Employees enjoy growth opportunities through mentorship, training programs, and cross‑functional projects. The company emphasizes work‑life balance with flexible work arrangements and a supportive environment for professional development.

Candidates can expect a structured interview process with two technical rounds focusing on programming, AI, and engineering concepts, followed by an HR round. The technical interviews are moderate in difficulty, often involving coding exercises, case studies, and scenario‑based questions. Preparation tips include practicing Python, reviewing AI fundamentals, and familiarizing oneself with wind turbine blade design principles.

Undergraduate, postgraduate, or doctoral students, as well as graduates who completed their degree within the past 12 months. No specific branch or percentage requirement is mentioned. Backlog policy is not specified.

Siemens Energy, a global leader in energy technology, has a rich legacy of innovation spanning over 150 years. As a key player in the wind industry, the company designs and manufactures cutting‑edge wind turbines that power millions of homes worldwide. The Bengaluru office is part of the Wind Power Technology Blades team, which focuses on developing high‑performance blades that meet stringent safety and efficiency standards. The company’s commitment to sustainability is reflected in its mission to accelerate the energy transition and reduce carbon emissions. The Automation / AI Intern for Blade Structural Design Process role is a full‑time internship based in Bengaluru. The intern will support the automation and digitalization of blade structural design workflows, applying engineering, programming, data analytics, and artificial intelligence to create intelligent solutions that enhance product risk assessment and engineering efficiency. Key responsibilities include: 1. Developing automation solutions for engineering risk assessment and compliance workflows. 2. Building dashboards and digital engineering tools using Python, data analytics, and AI technologies. 3. Improving traceability across requirements, risks, controls, and verification activities. 4. Evaluating AI and large language model applications for engineering knowledge management and workflow automation. 5. Collaborating with cross‑functional teams to gather requirements and translate them into technical solutions. 6. Performing data extraction and transformation from legacy systems into modern analytics platforms. 7. Supporting the creation of structured training materials for large‑scale wind turbine structural risk assessment. 8. Documenting code, processes, and best practices for future reference. 9. Participating in code reviews and ensuring adherence to quality standards. 10. Presenting findings and progress to senior engineers and stakeholders. The technical stack will involve Python, Excel automation, Power BI, MATLAB, and database technologies. Exposure to AI, machine learning, generative AI, and large language models is expected, along with familiarity with engineering workflow automation. Growth path: Interns gain practical experience in wind turbine blade engineering and AI‑enabled automation. They receive structured training, mentorship from industry experts, and an internship completion certificate that can serve as a stepping stone to a full‑time role within Siemens Energy or other renewable energy companies. Why join Siemens Energy? The company offers a collaborative, inclusive environment that values diverse perspectives. Employees work on real‑world challenges that drive the global energy transition, enjoy continuous learning opportunities, and benefit from a strong focus on work‑life balance and career development.