AI Engineer - Private Assets
Tasks
- Apply LLMs computer vision and deep learning to solve AI use cases
- Architect and implement end-to-end ML pipelines
- Design and implement enterprise scale AI architectures
- Develop NLP systems for information extraction and validation
- Implement MLOps for model deployment monitoring and continuous improvement
- Improve engineering productivity using AI agents
- Perform code reviews and maintain design documentation
- Provide AI ML technical guidance and mentorship
- Translate product requirements into scalable technical solutions
Perks/Benefits
- Advanced technology
- Career development
- Collaborative workspaces
- Employee resource groups
- Flexible working arrangements
- Global orientation program
- Inclusion programs
- Learning platform access
Skills/Tech-stack
Agent systems | Azure | CI/CD | Computer Vision | Continuous Learning | Conversational AI | Data Engineering | Deep learning | Distributed Systems | Google Cloud | LangFlow | Langchain | Language Models | Language Processing | Large Language Models | MLOps | Model Deployment | Model Monitoring | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | PyTorch | Python | RAG | Retrieval-Augmented Generation | Semantic Retrieval | Semantic Search | TensorFlow
Education
N/A
Roles
Regions
Countries
States
Cities
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