About the role

Develop, deploy, and maintain robust machine learning models and services while ensuring they remain performant and reliable in production. Collaborate with cross-functional teams to translate research prototypes into production-grade systems with proper testing, observability, and lifecycle management.

What they look for

Python Machine Learning PyTorch Scikit-learn NumPy Pandas Kubernetes FastAPI Flask MLOps Data Pipelines Model Monitoring Feature Engineering Statistics CI/CD Large Language Models

Requirements

Requires 4-6+ years of experience in machine learning or software engineering with a strong focus on productionizing ML models. Candidates must possess strong Python programming skills, experience with the scientific Python stack, and proficiency in containerized deployment environments like Kubernetes.

Full description

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.

Job Responsibilities• 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

QualificationsDegree 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

Equality StatementSmartstream is an equal opportunities employer. We are committed to promoting equality of opportunity and following practices which are free from unfair and unlawful discrimination.