Lead AI Engineer
Tasks
- Automate data pipelines for labeling versioning and exploration
- Build and deploy large language model solutions
- Create technical blogs and AI thought leadership
- Design reproducible training workflows
- Develop agentic systems
- Develop technical proposals with analysis and communication
- Evaluate model performance using evaluation frameworks
- Implement machine learning training pipelines
- Implement model deployment with monitoring and drift detection
- Lead mentor and manage AI and data engineering teams
- Manage experiment tracking and model registry
- Perform prompt engineering and fine-tuning
Perks/Benefits
- N/A
Skills/Tech-stack
AI Pipelines | AWS Bedrock | AWS SageMaker | AWS SageMaker Pipelines | Ansible | Anthropic API | Autogen | Automated testing | Azure ML | Azure ML pipelines | Azure Machine Learning | CI/CD | Canary Releases | Classification | Computer Vision | Containerization | CrewAI | Cross-validation | Data Versioning | Datadog | Dataiku | Docker | Drift Detection | Fairlearn) | Fine Tuning | Git | 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 | Model Deployment | Model Evaluation | Model Registry | Monitoring | NLP | OpenAI API | Prompt engineering | PyTorch | Python | Recommendation Systems | Reinforcement Learning | Responsible AI | Retraining | Sagemaker Pipelines | Scikit-learn | Series analysis | Serverless computing | TensorFlow | Terraform | Time Series | Time Series Analysis | 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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