1.68 Agentic AI/ML Engineer - Multimodal
Tasks
- Build agentic RAG pipelines
- Build evaluation pipelines
- Curate multimodal datasets
- Deploy multimodal models to production
- Detect drift and errors
- Evaluate vision and multimodal models
- Fine-tune multimodal models
- Implement embedding based memory and retrieval
- Implement model observability
- Improve model interpretability tools
- Optimize model inference
- Optimize multi GPU inference
- Train multimodal models
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Bfloat16 | CI/CD | Computer Vision | Data Pipelines | DeepSpeed | Distributed Multi GPU | Drift Detection | Embeddings | Experiment tracking | FP16 | FSDP | Few-Shot Learning | Few-shot | Huggingface | Knowledge graphs | Language Models | Large Scale Data | Large-scale | Large-scale Data Pipelines | LoRA | MLOps | Model Observability | Multi Vector RAG | Multi-GPU | Multimodal Learning | Open Vocabulary Detection | PyTorch | Python | QLoRA | Quantization | Retrieval-Augmented Generation | Temporal modeling | Token Chunking | VLLM | Vector Databases | Vector/RAG | Video Understanding | Vision Language Models | Vision-language | Zero-Shot | Zero-shot Learning
Education
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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