AI/ML Engineer Data Modeling

Job description

<p><p><b>Role Overview.</b><br/><br/> 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.<br/><br/> This role is ideal for someone who thrives at the intersection of machine learning, software engineering, and product thinking.<br/><br/> 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.<br/><br/> 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.<br/><br/> Youll be expected to own the full ML lifecyclefrom problem framing and experimentation to deployment, monitoring, and continuous improvement.<br/><br/> <b>Key Responsibilities ML Engineering : </p><p><br/></p><p>- Collaborate with product and analytics teams to identify high-impact personalization and automation opportunities.</p><p><br/></p> - Translate business problems into ML use cases, selecting appropriate modeling techniques </p><p>(e.g., classification, ranking, recommendation, summarization).<br/><br/></p><p> - Evaluate trade-offs between accuracy, interpretability, latency, and scalability to guide model </p><p>and architecture choices.<br/><br/></p><p>Model Development & Optimization : </p><p><br/></p><p>- Design and implement ML models using Python and frameworks like scikit-learn, XGBoost, TensorFlow, and PyTorch.</p><p><br/></p><p> - Apply advanced techniques such as feature selection, regularization, hyperparameter tuning </p><p>(Grid Search, Bayesian Optimization), and ensemble learning.<br/><br/></p><p> - Leverage transfer learning, fine-tuning, and prompt engineering to extend the capabilities of pre-trained LLMs.<br/><br/></p><p>LLM Integration & Extension : </p><p><br/></p><p>- Build and operationalize LLM-based services using Amazon Bedrock, LangChain, and vector databases (e.g., FAISS, Pinecone).<br/><br/></p><p> - Develop use cases such as intelligent summarization, contextual recommendations, and conversational personalization using retrieval-augmented generation (RAG) & Deployment : </p><p><br/></p><p>- Package and deploy models using Amazon SageMaker, SageMaker Inference Pipelines, AWS Lambda, and Kubernetes.<br/><br/></p><p> - Build containerized ML services and expose them via secure, versioned RESTful APIs using FastAPI or Flask.<br/><br/></p><p> - Integrate models into real-time and batch workflows, ensuring reliability and scalability.<br/><br/></p><p>Performance Monitoring & Governance :</p><p><br/></p><p>- Implement robust evaluation pipelines using metrics like AUC-ROC, F1-score, Precision/Recall, Lift, and RMSE, aligned with product KPIs.<br/><br/></p><p> - Monitor model drift, data quality, and prediction stability using tools like Evidently AI, SageMaker Model Monitor, and custom telemetry.<br/><br/></p><p> - Ensure model explainability, auditability, and compliance using MLflow, SageMaker Model Registry, SHAP, and LIME.<br/><br/></p><p>MLOps & Automation : </p><p><br/></p><p>- Automate end-to-end ML workflows using SageMaker Pipelines, Step Functions, and CI/CD tools like GitHub Actions, CodePipeline, and Terraform.<br/><br/></p><p> - Collaborate with platform engineers to ensure reproducibility, scalability, and adherence to security and privacy standards.<br/><br/></p><p>Core ML Algorithms & Techniques : </p><p><br/></p><p>- Supervised Learning : Logistic Regression, Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM).<br/><br/></p><p> - Unsupervised Learning : K-Means, DBSCAN, PCA, t-SNE.<br/><br/></p><p> - Deep Learning : CNNs, RNNs, Transformers (BERT, GPT), Autoencoders.<br/><br/></p><p> - Recommendation Systems : Matrix Factorization, Neural Collaborative Filtering, Hybrid </p><p>Models.<br/><br/></p><p> - NLP : Text Classification, Named Entity Recognition, Embeddings, RAG.<br/><br/></p><p> - Time Series Forecasting : ARIMA, Prophet, LSTM.<br/><br/></p><p> - Evaluation & Tuning : Cross-validation, Hyperparameter Optimization, A/B Testing.<br/><br/> <b>Qualifications :</b></p><p><br/></p><p>- Generative AI.</p><p><br/></p><p>- Applied Machine Learning & Deep Learning.<br/><br/></p><p> - Software Engineering Best Practices (SOLID, Design Patterns, CI/CD).<br/><br/></p><p> - Advanced Python Development.<br/><br/></p><p> - Cloud-Native ML Engineering (AWS SageMaker, Bedrock, etc.<br/><br/></p><p> - MLOps & Model Lifecycle Management.<br/></p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations