AI ML Engineer

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