AI/Machine Learning Engineer

Candidates typically undergo three rounds: an initial technical coding test, a second round focused on machine learning concepts and system design, and a final HR round. The interviews are known to be challenging yet fair, with a strong emphasis on problem‑solving, data structures, and practical ML knowledge. Preparation with coding practice and understanding of generative AI concepts can significantly improve performance.

Siemens Energy fosters an inclusive and collaborative environment where innovation is encouraged and continuous learning is supported. Employees benefit from structured career paths, mentorship programs, and a healthy work‑life balance that promotes both professional growth and personal well‑being.

Bachelor’s degree in Computer Science, Information Technology, Electrical Engineering, or related field with a minimum of 60% marks. No backlogs allowed. Candidates should have completed their degree in 2024 or 2025. Strong academic performance and project experience in AI/ML are essential.

Round 1: Technical coding and ML concepts → Round 2: System design/ML application interview → Round 3: HR and cultural fit

Siemens Energy, a global leader in energy technology, is dedicated to powering the future with sustainable and reliable solutions. With a workforce of over 100,000 employees across more than 90 countries, the company focuses on innovation in power generation, transmission, and digital solutions that drive the energy transition. The organization’s culture is built on collaboration, continuous learning, and a commitment to diversity, ensuring that every employee can contribute to transformative projects that impact millions worldwide. The AI/Machine Learning Engineer role is a fresh‑entry position that invites recent graduates or interns with a strong academic foundation in computer science or related fields. As part of the Digital Products and Solutions business unit, you will work closely with data scientists, software engineers, and product managers to design, develop, and maintain machine learning pipelines that deliver actionable insights and enhance user experiences. Your day-to-day responsibilities will include data preprocessing, model training, evaluation, and inference, as well as building simple Retrieval-Augmented Generation (RAG) and NLP workflows. Key responsibilities include: 1. Building and maintaining end‑to‑end ML and generative AI pipelines. 2. Developing RAG‑based and NLP workflows under senior guidance. 3. Performing text processing tasks such as cleaning, parsing, chunking, and generating embeddings. 4. Contributing to backend APIs (e.g., FastAPI) to expose ML/AI functionalities. 5. Integrating AI models into applications and supporting deployment activities. 6. Writing clean, modular, and testable Python code. 7. Collaborating with cross‑functional teams to deliver features. 8. Debugging, testing, and optimizing models for performance and reliability. 9. Staying updated on basic advancements in AI/ML and generative AI. 10. Participating in code reviews and knowledge sharing sessions. The technical stack you’ll encounter includes Python, NumPy, Pandas, scikit‑learn, PyTorch/TensorFlow, FastAPI, Git, and cloud platforms such as AWS or Azure. Exposure to generative AI concepts, LLMs, and prompt engineering is a plus. Growth Path: Starting as a junior engineer, you can progress to senior ML engineer, lead data science projects, or transition into AI product management. Siemens Energy offers continuous learning programs, mentorship, and opportunities to work on cross‑disciplinary projects that span from edge devices to cloud‑based analytics. Why Join: You’ll be part of a company that values innovation, sustainability, and employee growth. The role offers hands‑on experience with cutting‑edge AI technologies, a collaborative environment, and the chance to contribute to projects that shape the future of energy. With competitive compensation, comprehensive benefits, and a culture that celebrates diversity, Siemens Energy provides a platform for early‑career professionals to thrive.