AI Engineer
Tasks
- Apply prompt engineering
- Build LLMOps infrastructure
- Build multi-agent frameworks
- Build scalable backend services and REST APIs
- Deliver customer proof of concepts
- Deploy production ML systems
- Design and build agentic AI systems
- Develop ML pipelines
- Develop and deploy LLM-powered applications
- Develop evaluation frameworks
- Implement Responsible-AI guardrails
- Implement rag pipelines
- Integrate AI models into cloud on prem and hybrid environments
- Monitor models in production
- Perform fine-tuning
Perks/Benefits
Skills/Tech-stack
AWS | CI/CD | Cloud platform | Docker | Fine Tuning | Function Calling | Google Cloud | Google Cloud Platform | Guardrails | Human Feedback | Kubernetes | LLMOps | Langchain | Language Models | Large Language Models | Learning from Human Feedback | Llamaindex | Microsoft Azure | Model Evaluation | Prompt engineering | PyTorch | Python | REST APIs | Reinforcement Learning | Reinforcement Learning from Human Feedback | Responsible AI | Retrieval-Augmented Generation | Scikit-learn | TensorFlow | Tool use
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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