Madiff

AI Engineer

• Design and implement LangChain and LangGraph based GenAI workflows
• Integrate LLMs with structured and unstructured sports analytics data
• Build Python services exposing GenAI capabilities via APIs or internal tools
• Develop prompt strategies and multi step reasoning chains
• Implement evaluation, validation, and output quality controls
• Collaborate closely with sports analysts and domain experts
• Support fast iteration cycles and production deployment of GenAI components

GenAI Developer – Sports Analytics, Performance Intelligence

• Design and implement LangChain and LangGraph based workflows
• Build Python services exposing GenAI capabilities via secure APIs
• Integrate GenAI components with optimisation and customer analytics models
• Generate structured insights, summaries, comparisons, and scenario narratives
• Design and improve prompt strategies and multi step reasoning chains
• Implement evaluation, validation, and quality control mechanisms
• Collaborate closely with analytics and strategy stakeholders
• Support deployment and continuous improvement of GenAI services in production

GenAI Developer – Portfolio Optimisation, Customer Analytics

Risk

• Support creation and maintenance of metadata documentation including data ownership and business definitions
• Prepare business and vertical data lineage documentation
• Assist in documenting data quality controls and validation rules
• Participate in data transformation and data management activities
• Work with structured financial and risk datasets
• Collaborate with international stakeholders in English
• Ensure governance artefacts are aligned with regulatory standards

Junior Data Governance Analyst

• Design and implement LangChain and LangGraph based agent workflows
• Build Python services exposing GenAI capabilities via secure APIs
• Integrate LLMs with ML model outputs and structured enterprise data
• Develop RAG pipelines and vector based retrieval mechanisms
• Implement guardrails, validation layers, and evaluation logic for regulated outputs
• Collaborate with ML engineers and data scientists on end to end workflows
• Support deployment, monitoring, and optimisation in production environments
• Ensure explainability, traceability, and reliability of GenAI outputs

GenAI Developer – Credit Risk, Fraud Platform

Machine Learning Engineer

• Deploy and operate machine learning models supporting sports analytics use cases
• Build and maintain pipelines for model training, validation, and deployment
• Support high frequency and time series data processing workflows
• Deploy and manage LangChain and LangGraph based GenAI services
• Implement monitoring for model performance, drift, and data quality
• Ensure reproducibility and version control for models and datasets
• Support fast iteration and experimentation in production-like environments
• Collaborate closely with data scientists and engineers to streamline model lifecycle

MLOps Engineer – Sports Analytics, Performance Intelligence

• Build and maintain CI/CD pipelines for machine learning models
• Operationalise model lifecycle processes including versioning, promotion, and rollback
• Deploy and operate LangChain and LangGraph based services in production
• Implement monitoring for model performance, drift detection, and runtime failures
• Ensure traceability, auditability, and compliance readiness in regulated environments
• Collaborate with data scientists and ML engineers to support smooth model releases
• Optimise reliability, scalability, and security of ML and GenAI workloads

MLOps Engineer – Credit Risk and Fraud Platform

• Design and operate training and deployment pipelines for analytical and optimisation models
• Automate model retraining, validation, and promotion processes
• Ensure reproducibility and consistency across development, testing, and production environments
• Support scalable analytical workloads across cloud platforms
• Enable structured exposure of model outputs to LangChain and LangGraph workflows
• Monitor performance, stability, and reliability of ML pipelines
• Collaborate closely with data scientists and analytics teams to streamline experimentation to production