Generative AI Systems Engineer – Vision-Language Models
Tasks
- Align multimodal datasets
- Analyze dataset biases and distribution gaps
- Analyze model failure modes
- Architect scalable multimodal inference pipelines
- Benchmark accuracy latency cost
- Build image text metadata data pipelines
- Define evaluation metrics
- Design controlled experiments
- Evaluate pretrained vision language models
- Implement model serving APIs
- Implement parameter efficient fine tuning
- Optimize inference for low latency high throughput cost efficiency
- Optimize training under compute constraints
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | Batching | Caching | Containerization | Docker | Embedding Reuse | Experiment design | Fine Tuning | High Throughput | Inference Optimization | Language Models | LoRA | Low Latency | Model Evaluation | Multimodal Models | PyTorch | Python | QLoRA | Quantization | Vision Language Models | Vision-language
Education
Bachelor of Engineering | Bachelor of Science | Bachelor of Technology
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