Company Description Experian is a global data and technology company powering opportunities for people and businesses around the world We help to redefine lending practices uncover and prevent fraud simplify healthcare create marketing solutions and gain deeper insights into the automotive market all using our unique combination of data analytics and software We also assist millions of people to realize their financial goals and help them save time and money We operate across a range of markets from financial services to healthcare automotive agribusiness insurance and many more industry segments We invest in people and new advanced technologies to unlock the power of data As a FTSE 100 Index company listed on the London Stock Exchange EXPN we have a team of 22 500 people across 32 countries Our corporate headquarters are in Dublin Ireland Learn more at experianplc com Job description Role Overview We are seeking a Machine Learning Engineer to join a high-impact team within Experian Consumer Services ECS focused on building scalable reusable AI capabilities that power personalized financial experiences for millions of users This role is ideal for someone who thrives at the intersection of machine learning software engineering and product thinking You will work closely with product managers data scientists platform engineers and UX teams to understand consumer needs define ML-driven solutions and deliver production-grade AI services such as LLM-as-a-Service enterprise knowledge orchestration predictive intelligence APIs and personalized decisioning engines Success in this role requires not only strong technical skills but also the ability to evaluate trade-offs select the right models and tools and align ML solutions with business goals You ll be expected to own the full ML lifecycle from problem framing and experimentation to deployment monitoring and continuous improvement Key Responsibilities 1 Business-Aligned ML Engineering Collaborate with product and analytics teams to identify high-impact personalization and automation opportunities Translate business problems into ML use cases selecting appropriate modeling techniques e g classification ranking recommendation summarization Evaluate trade-offs between accuracy interpretability latency and scalability to guide model and architecture choices 2 Model Development Optimization Design and implement ML models using Python and frameworks like scikit-learn XGBoost TensorFlow and PyTorch Apply advanced techniques such as feature selection regularization hyperparameter tuning Grid Search Bayesian Optimization and ensemble learning Leverage transfer learning fine-tuning and prompt engineering to extend the capabilities of pre-trained LLMs 3 LLM Integration Extension Build and operationalize LLM-based services using Amazon Bedrock LangChain and vector databases e g FAISS Pinecone Develop use cases such as intelligent summarization contextual recommendations and conversational personalization using retrieval-augmented generation RAG pipelines 4 Productionization Deployment Package and deploy models using Amazon SageMaker SageMaker Inference Pipelines AWS Lambda and Kubernetes Build containerized ML services and expose them via secure versioned RESTful APIs using FastAPI or Flask Integrate models into real-time and batch workflows ensuring reliability and scalability 5 Performance Monitoring Governance Implement robust evaluation pipelines using metrics like AUC-ROC F1-score Precision Recall Lift and RMSE aligned with product KPIs Monitor model drift data quality and prediction stability using tools like Evidently AI SageMaker Model Monitor and custom telemetry Ensure model explainability auditability and compliance using MLflow SageMaker Model Registry SHAP and LIME 6 MLOps Automation Automate end-to-end ML workflows using SageMaker Pipelines Step Functions and CI CD tools like GitHub Actions CodePipeline and Terraform Collaborate with platform engineers to ensure reproducibility scalability and adherence to security and privacy standards 7 Core ML Algorithms Techniques Supervised Learning Logistic Regression Decision Trees Random Forests Gradient Boosting XGBoost LightGBM Unsupervised Learning K-Means DBSCAN PCA t-SNE Deep Learning CNNs RNNs Transformers BERT GPT Autoencoders Recommendation Systems Matrix Factorization Neural Collaborative Filtering Hybrid Models NLP Text Classification Named Entity Recognition Embeddings RAG Time Series Forecasting ARIMA Prophet LSTM Evaluation Tuning Cross-validation Hyperparameter Optimization A B Testing Qualifications Qualifications Generative AI Applied Machine Learning Deep Learning Software Engineering Best Practices SOLID Design Patterns CI CD Advanced Python Development Cloud-Native ML Engineering AWS SageMaker Bedrock etc MLOps Model Lifecycle Management Additional Information Our uniqueness is that we celebrate yours Experian s culture and people are important differentiators We take our people agenda very seriously and focus on what matters DEI work life balance development authenticity collaboration wellness reward recognition volunteering the list goes on Experian s people first approach is award-winning World s Best Workplaces TM 2024 Fortune Top 25 Great Place To Work TM in 24 countries and Glassdoor Best Places to Work 2024 to name a few Check out Experian Life on social or our Careers Site to understand why Experian is proud to be an Equal Opportunity and Affirmative Action employer Innovation is an significant part of Experian s DNA and practices and our diverse workforce drives our success Everyone can succeed at Experian and bring their whole self to work irrespective of their gender ethnicity religion colour sexuality physical ability or age If you have a disability or special need that requires accommodation please let us know at the earliest opportunity Experian Careers - Creating a better tomorrow together