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The Applied AI Director will lead efforts in fine-tuning open-source and proprietary LLMs for domain-specific tasks. The role requires expertise in deploying models using MLOps pipelines and optimizing model performance across cloud-native environments. Candidates should have strong proficiency in Python and ML libraries, along with experience in LLM fine-tuning techniques.
Job Responsibility:
Fine-tune open-source or proprietary LLMs for domain-specific tasks
Build classification, scoring, summarization, and anomaly detection models
Deploy models using MLOps pipelines (model versioning, drift detection, retraining)
Integrate models into workflows with human-in-the-loop oversight
Optimize model performance across cloud-native environments
Requirements:
Strong Python + ML libraries (PyTorch, Hugging Face, Transformers)
Experience with LLM fine-tuning (LoRA, PEFT, RAG techniques)
Familiarity with LangChain, vector DBs (Pinecone, FAISS), or similar tools
Experience in deploying models via Docker/Kubernetes or cloud ML services
Strong grasp of prompt engineering and AI safety best practices