Lead AI Engineer
Tasks
- Automate testing and quality checks
- Build automated data pipelines
- Build autonomous task execution frameworks
- Build infrastructure as code
- Create serverless CI CD pipelines
- Create technical blogs and thought leadership
- Deploy machine learning models on cloud
- Detect model drift and trigger retraining
- Develop and deploy agentic AI systems
- Implement containerization and orchestration
- Implement experiment tracking and reproducible training
- Implement retrieval-augmented generation
- Label and version data
- Lead and mentor AI and data engineers
- Manage model registry and monitoring
- Orchestrate multi-agent workflows
- Perform prompt engineering
- Run model fine tuning and evaluation
- Set up canary releases
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Bedrock | Ansible | Anthropic API | Artificial Intelligence | Autogen | Automated testing | Azure Machine Learning | CI/CD | Canary Releases | Computer Vision | Containerization | CrewAI | Cross-validation | Datadog | Dataiku | Docker | Drift Detection | Experiment tracking | Fine Tuning | Google Vertex | Google Vertex AI | Hugging Face | Hugging Face Transformers | Hyperparameter Tuning | Infrastructure as Code | Jupyter | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | MLflow | Machine Learning | Model Evaluation | Model Monitoring | Model Registry | NLP | OpenAI API | Prompt engineering | PyTorch | Reinforcement Learning | Responsible AI | Retraining | Retrieval-Augmented Generation | Scikit-learn | Serverless computing | TensorFlow | Terraform | Time Series | Torch | Version control | Vertex AI | Weigths and Biases | “as-code”
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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