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