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