Machine Learning Engineer

Tasks

• Build Agentic AI workflows
• Build Retrieval Augmented Generation systems
• Build regression suites
• Collaborate with data and platform engineers
• Communicate model tradeoffs to stakeholders
• Create feature stores and serving infrastructure
• Create tool using agents
• Deploy machine learning systems
• Design human in the loop pipelines
• Design offline and online evaluations
• Develop foundation model applications
• Explore data and build features
• Frame business problems for ML
• Implement multi-agent systems
• Iterate models using real world signals
• Maintain CI CD for ML
• Mentor engineers and lead design reviews
• Monitor model performance
• Own ML projects end-to-end
• Partner on MLOps pipelines
• Run A/B tests
• Set technical direction for ML stack
• Train and evaluate predictive models
• Use LLM as judge

Perks/Benefits

• N/A

Skills/Tech-stack

A/B | A/B Testing | Anomaly Detection | B testing | CI/CD | Computer Vision | Databricks | Experiment tracking | Fine Tuning | Function Calling | Hugging Face | JAX | LLMOps | Language Models | Language Processing | Large Language Models | LightGBM | MLOps | Model Monitoring | Multimodal | Natural Language | Natural Language Processing | NumPy | Pandas | Prompt engineering | PySpark | PyTorch | Python | Random Forest | Regression | Retrieval-Augmented Generation | Scikit-learn | Series forecasting | Structured outputs | Time Series | Time Series Forecasting | XGBoost