Job Description

Years of Experience:

4-10 Years

Skills Stack:

Python, PyTorch, TensorFlow, Keras, Transformers, LLM, Pandas, NumPy, SQL, MongoDB

Shift Timings:

05:30 am - 05:30 am ((UTC+05:30) Chennai, Kolkata, Mumbai, New Delhi)

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.

:

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

Locations:

Pune

Contact:

Recruitment Team

Email: tdg-recruitment@thedigitalgroup.com