AI Engineer
Tasks
- Build and deploy AI solutions end to end
- Deliver AI engineering projects
- Design prompt engineering and fine tuning workflows
- Develop LLM agentic systems
- Develop ML pipelines and training workflows
- Evaluate responsible AI models
- Implement AI delivery and governance
- Implement CI/CD and automated testing
- Implement model experiment tracking and reproducible runs
- Manage containerized applications with Docker and Kubernetes
- Manage model registry and deployment workflows
- Provision infrastructure using infrastructure-as-code
- Set up monitoring and retraining triggers
- Use serverless computing for AI workloads
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Bedrock | AWS SageMaker | Agent systems | Ansible | Anthropic API | Autogen | Automated testing | Azure ML | CI/CD | Containerization | CrewAI | Datadog | Dataiku | Docker | Fine Tuning | Git | Google Vertex | Google Vertex AI | Hugging Face | Hugging Face Transformers | Hyperparameter Tuning | Infrastructure as Code | Jupyter | Kubernetes | Langchain | Langgraph | MLflow | MLflow Model Registry | Model Monitoring | Model Registry | Model retraining | Multi-Agent | Multi-Agent Systems | OpenAI API | Prompt engineering | PyTorch | RAG Pipelines | Responsible AI | Retrieval-Augmented Generation | Scikit-learn | Serverless computing | TensorFlow | Terraform | Vertex AI | Weights and Biases | “as-code”
Education
N/A
Roles
AI | AI Engineer | Engineer | ML Engineer | MLOps Engineer
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