Senior Machine Learning Engineer
Tasks
- Build automated data and model pipelines
- Build generative AI solutions with LLMs
- Collaborate with clients on AI delivery
- Deploy models to production
- Design scalable machine learning architectures
- Detect data drift and model decay
- Develop AI agents and autonomous systems
- Develop end-to-end machine learning models
- Implement MLOps and AIOps practices
- Implement retrieval-augmented generation
- Mentor teams on machine learning best practices
- Monitor model performance continuously
- Perform feature engineering
- Prepare and explore data
- Train, validate, and optimize models
- Translate business needs into ML solutions
- Tune hyperparameters and run experiments
Perks/Benefits
- Conference and course access
- Hybrid work option
- Intensive schedule in summer
- Paid training and certifications
- Remote work option
- Telework support
Skills/Tech-stack
AIOps | AWS SageMaker | Airflow | Azure Databricks | Azure ML | Data Drift | Data Drift Detection | Deep learning | Docker | Drift Detection | Feature Engineering | Fine Tuning | Generative AI | Hyperparameter Tuning | Kubeflow | Kubernetes | LLM | Langchain | Language Processing | MLOps | MLflow | Machine Learning | Natural Language | Natural Language Processing | OpenAI API | Pandas | Pinecone | Prompt engineering | PyTorch | Python | RAG | Retrieval-Augmented Generation | Scikit-learn | TensorFlow | Transformers | Vector Databases | Vertex AI | Weaviate | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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