AI ML Engineer

Role Overview

Machine Learning Engineer

product managers

evaluate trade-offs

Key Responsibilities

• 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