What you'll be doing

You'll work across the full ML lifecycle, building production-grade AI solutions that transform real-time financial data into actionable insights.

• Projects include:Designing and deploying machine learning models for NLP, time-series forecasting and event detection
• Building LLM-powered applications including RAG pipelines, semantic search and summarisation
• Developing scalable model serving APIs using Python and Go
• Creating monitoring, retraining and model evaluation pipelines
• Working with financial datasets including earnings calls, news and company filings
• Collaborating with engineering teams to build reliable, low-latency AI infrastructure on AWS

• What they're looking for4+ years' experience building and deploying production ML systems
• Strong Python experience with solid backend engineering skills (Go is highly desirable)
• Experience with PyTorch, TensorFlow or Hugging Face
• Good understanding of transformers, embeddings and modern LLM architectures
• Experience building scalable data pipelines and ML infrastructure
• AWS experience (EKS, ECS, Lambda, S3, EC2)
• Comfortable working in a remote, distributed engineering team

• Nice to haveFintech or financial data experience
• Experience with RAG architectures and vector databases
• Kafka or streaming platforms
• MLOps tooling such as MLflow, Airflow or SageMaker
• Startup experience
• Open-source contributions
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