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Showing 8 jobs
Skills:
Tensorflow, Numpy, Pytorch, Docker, Dicom, Python, AWS, Pytest, Gcp, Opencv, Keras, FastAPI, Azure, Kubernetes, scikit-learn, Segmentation, TensorRT, MLflow, CI CD, CNNs, ONNX, Classical ML, Vision Transformers, nnU-Net, Weights Biases, Calibration, NIfTI, Clinical Metrics, MONAI, Detection
Skills:
Sqlalchemy, API security, PostgreSQL, Rabbitmq, Sqlite, Flask, FastAPI, Python, LangChain, Django ORM, AI ML development, document extraction, langFuse, large-scale RAG systems, Cloud Data Warehouses, OCR pipelines, LangGraph, RAG methodologies, langSmith, Java Spring Boot, fine-tuning LLMs
Skills:
Generative AI and Large Language Models (LLMs), Microsoft Azure, FastAPI, Python, Azure OpenAI AI services, RAG embeddings vector semantic search and prompt engineering, Authentication and API security concepts, REST API design and integration, Docker and containerised application deployment, Git and version control, SQL and relational databases, Application debugging logging and troubleshooting
Skills:
large language models (LLMs), multi-agent workflows, embedding-powered retrieval solutions, GenAI-based systems
Skills:
Api Development, Tensorflow, Pytorch, Gcp, Azure, Python, AWS, LangChain, Airflow, Prompt engineering, Model deployment, Scikit-learn, Hugging Face, MLflow, Kubeflow, MLOps tools, LLM frameworks, Data workflows, LlamaIndex, ETL pipelines
Skills:
AWS, FastAPI, Python, Kubernetes, Docker, EKS
Skills:
Tensorflow, Numpy, Pytorch, Docker, XGBoost, Python, Pandas, MLops, Azure Machine Learning, scikit-learn, vector databases, Azure AI Foundry, LangGraph, Azure AI Search, Application Insights, LangChain, experiment tracking, RAG architecture, LLMOps, Azure OpenAI Service, LightGBM, embedding models, Semantic Kernel, CI/CD pipelines, Azure AI Content Safety, Azure App Service Functions
Skills:
software engineering practices , Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG), Apis, Microservices, MLops, Python, AI development frameworks, LLMOps, architecture patterns, semantic models, prompt engineering, cloud environments, agentic AI architectures, DataOps, knowledge and context graphs, CI/CD, modern AI methodologies, cloud-native development
