ML Developer

About the Role We are looking for an ML Developer with 6+ years of experience to design, build, and deploy machine learning solutions supporting the surveillance function within a financial services environment. You will apply advanced ML techniques - anomaly detection, NLP, behavioural analytics, and classification models - to enhance communication and trade surveillance capabilities, helping detect market abuse, misconduct, and compliance breaches at scale. Key Responsibilities Design, develop, and deploy ML models for surveillance use cases - anomaly detection, behavioural analytics, NLP-based communication analysis, and pattern recognition across large-scale financial data. Build and maintain NLP pipelines - text classification, sentiment analysis, entity recognition, and semantic similarity - for electronic communication surveillance. Develop and maintain feature engineering pipelines - extracting, transforming, and selecting relevant features from trade, communication, and market data for ML model training. Train, evaluate, and optimise ML models - applying supervised, unsupervised, and semi-supervised learning techniques for surveillance detection and alert prioritisation. Integrate ML models into surveillance platforms - deploying models as REST APIs or batch inference pipelines for production consumption. Implement model monitoring and drift detection - ensuring surveillance models maintain accuracy, reliability, and performance in production. Collaborate with surveillance analysts, compliance teams, and data engineers - translating surveillance business requirements into well-designed ML solutions. Support alert tuning and false positive reduction - applying ML-driven approaches to improve surveillance detection quality and operational efficiency. Produce clear technical documentation - model design documents, feature specifications, and deployment runbooks. What We're Looking For 6+ years of ML development experience with strong Python and machine learning expertise in production environments. Strong ML skills - supervised, unsupervised, and semi-supervised learning - applied to anomaly detection, classification, and behavioural analytics use cases. Proven NLP expertise - text classification, sentiment analysis, named entity recognition, and semantic search using spaCy, NLTK, Hugging Face, or equivalent. Strong Python proficiency - NumPy, pandas, Scikit-learn, TensorFlow, or PyTorch - for ML model development and data processing. Experience with feature engineering - extracting and transforming features from structured and unstructured financial data for ML model training. Hands-on ML model deployment experience - REST API serving, batch inference, and containerised model deployment using Docker. Experience with MLflow or equivalent - experiment tracking, model versioning, and model registry management. Solid SQL skills - data querying and validation across large-scale financial datasets. Good understanding of financial services compliance or surveillance domain - communication surveillance, trade surveillance, or market abuse detection. Strong AWS experience - SageMaker, S3, Lambda, or equivalent - for ML model training and deployment. Excellent communication skills - presenting ML findings, model performance, and surveillance insights to compliance and technology stakeholders. Nice to Have Experience with LLMs and GenAI - GPT, Claude, or Llama - for advanced communication surveillance and intelligent alert summarisation. Familiarity with surveillance platforms - NICE Actimize, Behavox, or equivalent - and their ML integration patterns. Knowledge of graph-based ML - network analysis and entity relationship modelling for market abuse detection. Exposure to real-time streaming ML - Kafka or AWS Kinesis - for real-time surveillance alert generation. Experience with explainability frameworks - SHAP, LIME - for model transparency and regulatory audit support. Knowledge of regulatory frameworks - MiFID II, MAR, ASIC Market Integrity Rules - and their surveillance implications. AWS Certified Machine Learning Specialty certification. Experience in Financial Services, Banking, or regulated industry environments.