Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer to build, ship, and operate the machine learning and AI solutions at the core of SmartStream’s financial data processing and reconciliation platforms. Working with our data scientists, you will turn prototypes into cohesive, production-ready systems, using large and complex financial transaction datasets to power capabilities such as transaction matching, reconciliation, and exception handling. You will work across the full spectrum of applied AI, from classical machine learning (supervised, unsupervised, and deep learning) to agentic AI solutions built on large language models, tool use, and multi-step reasoning. This is a hands-on engineering role. The emphasis is on productionising: turning models into robust, well-tested, observable services and keeping them accurate and reliable in production. You will own existing ML services end to end and evolve them, working closely with software engineers, data scientists, product managers, and domain experts to turn real-world reconciliation challenges into dependable software., * Develop, deploy, and maintain machine learning models and services, and keep existing ones performant and robust

• Translate research artefacts and prototypes into production-grade ML systems: hardening code, adding tests and observability, and owning deployment, scaling, and lifecycle management.
• Own model serving, monitoring, drift detection, and retraining in production
• Engineer and evaluate features on real financial datasets, and calibrate and validate models for reliable behaviour
• Collaborate with software engineers and data scientists on the surrounding data and matching platform
• Document methods and decisions to keep models transparent and reproducible

• Strong software engineering in Python: clean, typed, well-tested code, version control, and CI/CD
• Strong proficiency with the scientific Python stack (NumPy, Pandas, scikit-learn, PyTorch) and a solid, practical grasp of machine learning, statistics, and model evaluation
• Experience taking ML models into production and operating them there (serving, monitoring, retraining), not just building them in notebooks
• Experience building and running production services and APIs (e.g. FastAPI or Flask), containerised and deployed on Kubernetes or similar
• Feature engineering on structured/tabular data, and sound model evaluation and validation
• Ability to work with large datasets and build reliable data pipelines
• Clear communication with technical and business stakeholders

Desirable Skills

• MLOps practices: model and data versioning, automated retraining, monitoring, and champion/challenger evaluation
• Distributed data processing (e.g. Dask, Spark, Apache Arrow/parquet) and handling columnar data at scale
• Deeper neural-network / PyTorch experience
• Model explainability (e.g. SHAP) and probability calibration
• Experience with LLM-based or agentic systems (tool use, orchestration, retrieval)
• Familiarity with workflow orchestration and event/stream processing is a plus
• Experience in regulated or data-intensive industries, ideally financial services

• Familiarity with cloud-based ML infrastructure, Degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience Experience

• 4-6+ years in machine learning engineering, or software engineering with a strong ML component
• Experience delivering and operating ML models in production
• Experience working in cross-functional teams delivering software products
• Strong problem-solving skills and a pragmatic, ownership-driven approach to shipping reliable software