AI Engineer
Tasks
- Block sensitive content
- Build context engines for personalization
- Build evaluation benchmarks
- Build multi intent understanding
- Build multi zone control
- Compress and optimize models for edge deployment
- Design agent orchestration architectures
- Develop planning and memory systems
- Ensure production stability with MLOps
- Evaluate LLM performance
- Fine-tune LLMs
- Implement AI guardrails for hallucination control
- Implement RAG for automotive domains
- Implement function tool calling
- Manage routing between domains
- Optimize on device inference
- Protect personal data
- Research LLMs
- Resolve task conflicts
- Train recommendation models
Perks/Benefits
- N/A
Skills/Tech-stack
Agent Orchestration | Agent systems | Computer Vision | Containerization | Context engineering | Function Calling | Huggingface | Knowledge Distillation | LLMOps | Language Models | Language Processing | Large Language Models | MLOps | Memory systems | Model Compression | Model Serving | Monitoring | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | ONNX | ONNX Runtime | Planning | Prompt engineering | Pruning | PyTorch | Python | Quantization | RAG | Speech Recognition | TFLite | TensorRT | Text to Speech | Tool use | Transformers | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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