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Software Engineer (AI / ML)

Software Engineer (AI / ML)

Experianhyderabad, India
13 hours ago
Job description

Job Description

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

    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
  • Additional Information

    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.

    Experian Careers - Creating a better tomorrow together

    Find out what its like to work for Experian by clicking here

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