Norton_AI/MLEngineer

The AI/ML Engineer will design, build, and deploy supervised, unsupervised, and deep learning models, owning end-to-end NLP workflows from pre-processing to fine-tuning. This role requires hands-on experience with Sentence Transformers for semantic embeddings and integrating FAISS or Qdrant for scalable vector similarity search. The engineer will architect models for NER, relation extraction, intent classification, and LLM-based generation, integrating them with production-grade APIs. Expertise in Linux/Windows ML environments, MLOps best practices, and model optimization is essential, along with deploying and managing models on cloud platforms.

Must Have

• 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.

Good to Have

• Understanding of Azure Pipelines, Docker, Kubernetes (AKS) if required.
• Understanding of GPU/CPU resource allocation, model parallelization, and scaling.

Role and Responsibility Details:

• 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.

Job Description:

• 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.

Qualifications:

MTech, B.Tech, B.E