ML Lead Engineer

Tasks

• Architect machine learning systems
• Automate retraining pipelines
• Build data pipelines
• Build geospatial analytics models
• Build machine learning services
• Build recommendation systems
• Create personalization models
• Define AI engineering best practices
• Deploy machine learning models
• Deploy models to production
• Detect anomalies
• Develop churn prediction
• Develop forecasting systems
• Develop machine learning APIs
• Enable real time decision systems
• Establish coding standards
• Implement MLOps frameworks
• Maintain model registries
• Manage feature stores
• Mentor machine learning engineers
• Monitor model performance
• Optimize inference for latency
• Optimize inference for reliability
• Optimize inference for scalability
• Optimize networks
• Own machine learning lifecycle
• Partner with engineering teams
• Perform feature engineering
• Retrain models
• Scale production machine learning systems
• Set up CI/CD pipelines
• Support solution governance
• Track experiments
• Train machine learning models
• Translate AI prototypes into production systems
• Validate models

Perks/Benefits

• N/A

Skills/Tech-stack

AWS | Anomaly Detection | Apache Airflow | Apache Spark | Batch Processing | CI/CD | Deep learning | Distributed Computing | Docker | Ensemble learning | Explainability | Feature Engineering | Feature Store | Forecasting | GenAI | Geospatial Analytics | Google Cloud | Governance | Graph analytics | Kubernetes | LLM | MLflow | Microservices | Microsoft Azure | Model Monitoring | Model Registry | PyTorch | Python | Real Time | Real-time Inference | Recommendation Systems | Scikit-learn | Series forecasting | Statistical modeling | Streaming Pipelines | Supervised Learning | TensorFlow | Time Series | Time Series Forecasting | Unsupervised Learning