Remote Machine Learning Engineer, Software Engineer

Summary

Join Scalable Capital's Data Department as a ML Engineer to lay the foundations for AI/ML technologies, drive architecture development, build infrastructure components, and collaborate with cross-functional teams.

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

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