Software Engineer

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

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

• 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

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 2024 (Fortune Top 25), Great Place To Work 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 important 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.

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