Norton_AI/MLEngineer

" Design, build, and deploy supervised, unsupervised, and deep learning models. " Own end-to-end NLP workflows: pre-processing, training, evaluation, fine-tuning. " Hands-on experience in building and deploying models using Sentence Transformers for semantic embeddings and integrating FAISS or Qdrant for scalable vector similarity search and retrieval. " Strong understanding of handling limited data scenarios using data augmentation strategies and Monte Carlo simulation techniques to improve model robustness and accuracy. " Architect models for NER, relation extraction, intent classification, and LLM-based generation like Mistral, LLama etc. " Integrate ML models with production-grade APIs and Flask/FastAPI endpoints. " Expertise in Linux/Windows-based ML environments and managing dependencies and virtual environments (venv, conda, pip). " Work with MLOps best practices: model versioning, reproducibility, monitoring, and rollback strategies. " Optimize models for speed/memory, leveraging techniques like quantization, ONNX, or TorchScript. " Deploy and manage machine learning models on cloud platforms using services like ML pipelines, object storage, serverless functions, and container orchestration tools. " Understanding of Azure Pipelines, Docker, Kubernetes (AKS) if required. " Understanding of GPU/CPU resource allocation, model parallelization, and scaling.

• Design, build, and deploy supervised, unsupervised, and deep learning models.
• Own end-to-end NLP workflows: pre-processing, training, evaluation, fine-tuning.
• Hands-on experience in building and deploying models using Sentence Transformers for semantic embeddings and integrating FAISS or Qdrant for scalable vector similarity search and retrieval.
• Strong understanding of handling limited data scenarios using data augmentation strategies and Monte Carlo simulation techniques to improve model robustness and accuracy.
• Architect models for NER, relation extraction, intent classification, and LLM-based generation like Mistral, LLama etc.
• Integrate ML models with production-grade APIs and Flask/FastAPI endpoints.
• Expertise in Linux/Windows-based ML environments and managing dependencies and virtual environments (venv, conda, pip).
• Work with MLOps best practices: model versioning, reproducibility, monitoring, and rollback strategies.
• Optimize models for speed/memory, leveraging techniques like quantization, ONNX, or TorchScript.
• Deploy and manage machine learning models on cloud platforms using services like ML pipelines, object storage, serverless functions, and container orchestration tools.
• Understanding of Azure Pipelines, Docker, Kubernetes (AKS) if required.

Understanding of GPU/CPU resource allocation, model parallelization, and scaling.