Machine Learning Engineer

DO:

• •Highlight model impact on users or revenue.
• •Mention ML frameworks (TensorFlow, PyTorch, HuggingFace).
• •Include model accuracy or precision improvements.
• •Show deployment methods (SageMaker, Vertex AI, Lambda).

DON'T:

• •Leave out model performance metrics.
• •Use generic statements like 'built ML models' without context.
• •Exclude business relevance of models.
• •Forget to include retraining and monitoring workflows.

DO:

• •Include CI/CD for ML (Airflow, MLflow, DVC).
• •Show retraining, monitoring, or auto-scaling capabilities.
• •Highlight cost or time savings achieved through automation.
• •Mention cloud-native ML platforms used.

DON'T:

• •Ignore infrastructure costs related to ML deployment.
• •Fail to include collaborative cross-functional aspects.
• •Omit version control or explainability methods.
• •Leave out production readiness considerations.

DO:

• •Include publications, patents, or whitepapers.
• •Showcase fine-tuning and transfer learning techniques.
• •Highlight open-source contributions if applicable.
• •Mention experimentation at scale (A/B testing, model selection).

DON'T:

• •Exclude research or publications if relevant.
• •Use too much academic jargon for an industry role.
• •Forget to include model explainability tools (SHAP, LIME).
• •Ignore interdisciplinary collaboration (data, product, infra teams).

Performance Metrics:

• •Improved prediction latency by 65%
• •Achieved 93% precision in fraud detection model
• •Increased NPS prediction accuracy by 22%
• •Reduced manual ML intervention by 80%

Scale Metrics:

• •Served ML models to 10M+ users
• •Productionized 6+ ML features for SaaS platform
• •Handled 2M+ daily predictions with serverless inference
• •Built feature stores integrated with Airflow + BigQuery

Business Metrics:

• •Reduced operational costs by $1M annually
• •Improved early detection rates by 30% in healthcare models
• •Enabled personalization for 4 global clients
• •Increased product adoption through AI features

ML Frameworks:

TensorFlow, PyTorch, LightGBM, HuggingFace Transformers, Scikit-learn

MLOps & Tools:

Airflow, MLflow, DVC, Kubeflow, Vertex AI, SageMaker

Techniques:

Deep Learning, Transfer Learning, Time Series Forecasting, NLP, Model Explainability