Principal Machine Learning Engineer
Tasks
- Architect LLM/RAG solutions
- Build end-to-end ML pipelines
- Build production machine learning and generative AI solutions
- Contribute reusable ML and GenAI platform components
- Drive adoption in regulated environments
- Enforce responsible AI data quality and lineage
- Establish observability and operational excellence
- Integrate model services with user interfaces
- Lead architecture reviews and technical governance
- Lead model evaluation and drift detection
- Own enterprise AI/ML architecture
- Perform exploratory data analysis and feature ideation
- Prototype and benchmark new algorithms
- Translate domain needs into technical roadmaps
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS | Agile | Auditability | Azure | B testing | Blue-Green Deployment | Blue/green | Canary | Canary Deployment | Cloud platform | Clustering | Convolutional Neural Networks | Data Lineage | Data Quality | Deep learning | Dimensionality Reduction | Docker | Experiment tracking | Explainability | Feature Stores | GitHub Actions | Google Cloud | Google Cloud Platform | Java | Kubeflow | Kubernetes | Langchain | Langgraph | Language Models | Language Processing | Large Language Models | Machine Learning | Model Evaluation | Model Registry | Natural Language | Natural Language Processing | Neural Networks | Privacy controls | Prompt engineering | Python | Recurrent Neural Networks | Regression | Retrieval-Augmented Generation | SAFe | SLO | Sagemaker Pipelines | Semantic Kernel | Time Series | Transformers | Vector Databases
Education
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