Lead AI Engineer
Tasks
- Build agentic systems
- Conduct model fine-tuning
- Create technical blogs on AI
- Deliver AI thought leadership
- Deploy machine learning models
- Develop LLM applications
- Develop machine learning models
- Enable model monitoring and drift detection
- Evaluate responsible AI systems
- Implement data pipelines
- Lead AI and data engineering team
- Manage MLOps lifecycle
- Mentor AI and data engineers
- Perform prompt engineering
- Solve AI problems
- Track experiments
- Trigger model retraining
- Tune hyperparameters
Perks/Benefits
- N/A
Skills/Tech-stack
AI Pipelines | AWS Bedrock | AWS SageMaker | Ansible | Anthropic API | Autogen | Automated testing | Azure ML | Azure ML pipelines | Azure Machine Learning | CI/CD | Canary Releases | Classification | Cloud Computing | Computer Vision | CrewAI | Cross-validation | Data Versioning | Data labeling | Datadog | Dataiku | Docker | Drift Detection | Experiment logging | Experiment tracking | Fine Tuning | Google Vertex | Google Vertex AI | Hugging Face | Hugging Face Transformers | Hyperparameter Tuning | Infrastructure as Code | Jupyter | Kubeflow | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | ML pipelines | MLflow | Machine Learning | Microsoft Fairlearn | Model Deployment | Model Evaluation | Model Registry | Monitoring | NLP | OpenAI API | Prompt engineering | PyTorch | Recommendation Systems | Reinforcement Learning | Retraining Pipelines | Sagemaker Pipelines | Scikit-learn | Serverless computing | TensorFlow | Terraform | Time Series | Version control | Vertex AI | Vertex AI pipelines | Weights and Biases | “as-code”
Education
N/A
Roles
AI | AI Engineer | Engineer | Lead | Lead AI Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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