AI/Machine Learning Engineer

Siemens Energy typically conducts a three‑stage interview process: an online aptitude and coding test, followed by a technical interview focusing on machine‑learning fundamentals, Python coding, and project experience, and finally an HR round assessing cultural fit and communication skills. The difficulty is moderate to high; thorough preparation on ML concepts, coding efficiency, and clear articulation of past projects is essential.

Siemens Energy promotes a collaborative and inclusive work culture where innovation is encouraged and employees are empowered to take ownership of their projects. The company offers structured career growth, regular learning opportunities, and a balanced work‑life environment supported by flexible policies.

Bachelor's degree in Computer Science, Information Technology, Electronics & Communication, or related engineering discipline; minimum 60% aggregate (or CGPA 6.0/10); batch years 2022‑2026; no active backlogs at the time of joining; 0‑3 years of relevant project or internship experience in AI/ML.

Round 1: Online assessment (aptitude + coding) → Round 2: Technical interview (ML concepts, Python coding, project discussion) → Round 3: HR interview (fitment, motivations, company knowledge)

Siemens Energy is a global leader in energy technology, employing around 100,000 professionals across more than 90 countries. With a legacy of 150 years, the company drives the energy transition by delivering sustainable, reliable, and affordable power solutions. Its portfolio spans power generation, transmission, and digital services, enabling a greener future for industries and communities worldwide. The Digital Products and Solutions business unit focuses on leveraging data, analytics, and AI to enhance product performance, operational efficiency, and customer experience. As an AI/Machine Learning Engineer in the EVP Global Functions team, you will be at the forefront of building intelligent solutions that translate raw data into actionable insights. Working closely with data scientists, software engineers, and product managers, you will design, develop, and maintain end‑to‑end machine‑learning pipelines that power Siemens Energy’s digital offerings. This role offers a unique blend of research‑oriented tasks and production‑grade engineering, giving fresh graduates a fast‑track exposure to real‑world AI applications. **Key Responsibilities** 1. Assist in building and maintaining ML and basic Generative AI pipelines covering data preprocessing, model training, evaluation, and inference. 2. Support the development of Retrieval‑Augmented Generation (RAG) and NLP workflows under senior guidance. 3. Perform text‑processing tasks such as cleaning, parsing, chunking, and generating embeddings. 4. Develop and expose backend APIs (e.g., FastAPI) to serve AI/ML functionalities to internal and external applications. 5. Integrate AI models into larger software systems and assist with deployment on cloud platforms. 6. Write clean, modular, and testable Python code following industry best practices and coding standards. 7. Collaborate with cross‑functional teams to translate business requirements into technical solutions. 8. Debug, test, and optimise models and pipelines for performance, scalability, and reliability. 9. Keep abreast of emerging trends in AI/ML, especially Generative AI, and propose innovative ideas. 10. Document code, experiments, and processes to ensure knowledge sharing within the team. **Tech Stack**: Python, FastAPI, NumPy, Pandas, scikit‑learn, PyTorch/TensorFlow, HuggingFace Transformers, AWS/Azure (basic), Git, Docker (optional), RAG frameworks, NLP libraries (spaCy, NLTK). **Growth Path**: Starting as an entry‑level ML Engineer, you can progress to Senior ML Engineer, then to Lead Engineer or AI Solutions Architect, with opportunities to move into product management or specialized research roles within Siemens Energy’s digital ecosystem. **Why Join Siemens Energy?** The company offers a vibrant, innovation‑driven environment where fresh talent can work on cutting‑edge AI projects that have a tangible impact on the world’s energy landscape. Employees benefit from robust learning programs, mentorship from industry veterans, and a culture that values sustainability, diversity, and continuous improvement.