Senior ML Engineer / Software Engineer Machine Learning & AI

At Scalable Capital, we are thrilled to expand our Data Department with multiple new ML Engineering roles in our growing Data Science team. 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. In any case, in this newly formed work stream, you will have the unique opportunity to lay the foundations and set the right direction.

Responsibilities:

• Identify, evaluate, implement, and maintain (Gen) AI / ML technologies for internal but also potential future client-serving services by following software engineering best practices
• Drive the development of appropriate architectures for deploying and maintaining scalable ML/(Gen) AI solutions
• Build infrastructure components, CI/CD pipelines, configure, extend, and maintain our existing ML services on AWS
• Evaluate retrieval techniques, language models, and generative AI methodologies by not losing your focus on pragmatic solutions
• Implement automated testing and monitoring techniques to ensure the accuracy and reliability of AI systems
• Collaborate with cross-functional teams to ensure the successful integration of AI systems into business processes
• Stay up to date with the latest industry developments and technologies to ensure our solutions remain at the forefront of innovation

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field
• A generalist mindset to continuously learn and open to switch between different technical domains like Backend, Frontend, AI/ML Infrastructure
• Extensive experience in AI/ML technologies and software development (Python)
• Experience with building frontends (e.g., Next.js would be a big plus)
• Experience with dockerization, cloud platforms, preferably AWS (ECS, Lambdas, API Gateway,...), and related ML/GenAI services such as AWS Bedrock, Sagemaker
• Familiarity with building CI/CD pipelines (e.g., Jenkins, GitHub Actions) and version control practices
• Confidence in working with modern machine learning libraries such as scikit-learn, PyTorch, Transformers, Langchain, LlamaIndex
• Strong understanding of chains, routing, agents, Retrieval-Augmented Generation (RAG), and the use of vector databases for managing structured and unstructured data sources
• 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
• Ideally hands on experience with model training, fine-tuning, evaluation, optimization, risk mitigation even in production environments
• Experience with Infrastructure as Code (e.g., terraform)
• Interest in financial services and markets is a plus
• Strong project management and organisational skills paired with excellent problem solving skills and hands on mentality