Foundation Model Engineer
Tasks
- Apply generative AI advancements to improve systems
- Build data preprocessing and automation scripts
- Curate training datasets for supervised fine tuning
- Deploy and monitor machine learning models using MLOps
- Design model training and fine tuning pipelines
- Develop fine tuning workflows for large language models
- Document experiments and technical designs
- Evaluate model performance using automated metrics
- Implement parameter efficient fine tuning
- Integrate LLM solutions into production applications
- Optimize model inference latency and resource utilization
- Troubleshoot training issues and tune hyperparameters
Perks/Benefits
Skills/Tech-stack
AWS | Azure | CI/CD | Cloud platform | DeepSpeed | Docker | Dspy | Embeddings | FSDP | Git | Google Cloud | Google Cloud Platform | Hugging Face | Hugging Face Transformers | Kubeflow | Kubernetes | Langchain | Language Processing | Llamaindex | LoRA | ML Lifecycle | MLOps | MLflow | Natural Language | Natural Language Processing | ONNX Runtime | PEFT | Prompt engineering | PyTorch | Python | QLoRA | RAG | Reinforcement Learning | Retrieval-Augmented Generation | Supervised Learning | TensorRT-LLM | Transformers | VLLM | Vector Databases | Weights and Biases
Education
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