(Senior) ML Engineer / Software Engineer Machine Learning & AI (m/f/x) onsite or remote in Germany

<h3>Company Description</h3><p>Scalable Capital is a leading digital investment platform in Europe that makes investing easy and affordable for everyone. Clients of the Scalable Broker can trade 8,000 stocks, 2,500 ETFs, and 3,500 funds and other exchange traded products to build their portfolios, earn interest on their cash balance and take secured loans. People can also have their investments professionally managed via the digital wealth management service. More than one million clients already use the services.</p><p>Scalable Capital was founded in 2014 and is active in Germany, Austria, France, Italy, the Netherlands, Spain, and the UK. The investment firm, which is supervised by BaFin and the Bundesbank, has more than 20 billion euros on its platform. In addition to its business for private clients, the company operates B2B solutions. Its long-standing partners include ING, Barclays Bank in the UK, the robo-advisor Oskar, and the Santander Group in Spain. Scalable Capital employs more than 500 people at its offices in Munich, Berlin, and London. Together with the founding and management team around Erik Podzuweit and Florian Prucker, they strive to empower everyone to become an investor.</p><p>Visit our <a href="https://www.google.com/url?q=https://de.scalable.capital/blog&amp;sa=D&amp;source=calendar&amp;ust=1709974557945714&amp;usg=AOvVaw1-ii78nD4LIEkV3O9MEWhg">finance blog</a> or check out our Social Media channels to find out what our Expert Teams have to say.<br/> <br/> Our Company Values guide us every day in how we work and collaborate. To learn more about them, you can find our values <a href="https://www.google.com/url?q=https://de.scalable.capital/values-principles&amp;sa=D&amp;source=calendar&amp;ust=1709974557945714&amp;usg=AOvVaw1LP9L3MAFXhHG8Otw2-KHC">here</a> (English).</p><h3>Job Description</h3><p>At Scalable Capital, we are thrilled to expand our Data Department with multiple new ML Engineering roles in our growing Data Science team. <strong>Depending on the applicant's profile, this role can either focus more on the infrastructure side (i.e., MLOps) or bridge the gap between software development and Machine Learning</strong>. In any case, in this newly formed work stream, you will have the unique opportunity to lay the foundations and set the right direction.</p><p><u>Responsibilities</u>:</p><ul><li>Identify, evaluate, implement, and maintain (Gen) AI / ML technologies for internal but also potential future client-serving services by following software engineering best practices</li><li>Drive the development of appropriate architectures for deploying and maintaining scalable ML/(Gen) AI solutions</li><li>Build infrastructure components, CI/CD pipelines, configure, extend, and maintain our existing ML services on AWS</li><li>Evaluate retrieval techniques, language models, and generative AI methodologies by not losing your focus on pragmatic solutions</li><li>Implement automated testing and monitoring techniques to ensure the accuracy and reliability of AI systems</li><li>Collaborate with cross-functional teams to ensure the successful integration of AI systems into business processes</li><li>Stay up to date with the latest industry developments and technologies to ensure our solutions remain at the forefront of innovation</li></ul><p> </p><h3>Qualifications</h3><ul><li>Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field</li><li>A generalist mindset to continuously learn and open to switch between different technical domains like Backend, Frontend, AI/ML Infrastructure</li><li>Extensive experience in AI/ML technologies and software development (Python)</li><li>Experience with building frontends (e.g., Next.js would be a big plus)</li><li>Experience with dockerization, cloud platforms, preferably AWS (ECS, Lambdas, API Gateway,...), and related ML/GenAI services such as AWS Bedrock, Sagemaker</li><li>Familiarity with building CI/CD pipelines (e.g., Jenkins, GitHub Actions) and version control practices</li><li>Confidence in working with modern machine learning libraries such as scikit-learn, PyTorch, Transformers, Langchain, LlamaIndex</li><li>Strong understanding of chains, routing, agents, Retrieval-Augmented Generation (RAG), and the use of vector databases for managing structured and unstructured data sources</li><li>Familiarity with MLOps practices, understanding the lifecycle of ML model development and deployment, performance monitoring and how this can be also applied to LLM use cases</li><li>Ideally hands on experience with model training, fine-tuning, evaluation, optimization, risk mitigation even in production environments</li><li>Experience with Infrastructure as Code (e.g., terraform)</li><li>Interest in financial services and markets is a plus</li><li>Strong project management and organisational skills paired with excellent problem solving skills and hands on mentality</li></ul><h3>Additional