Devops, Machine Learning, MLops, Scala, Python, Generative AI, Vector databases, Feature stores

Tensorflow, Pytorch, Python, MLops, LLMs, cloud AI platform, prompt engineering, Generative AI

Apis, Gcp, Docker, Distributed Systems, Azure, Kubernetes, Python, AWS, vector search, data pipelines, LLMs, AI ML architecture, Recommendation systems, Embeddings, multimodal AI, RAG pipelines

snowflake , Java, MLops, Gcp, Docker, Databricks, Azure, Kubernetes, Python, AWS, Generative AI, LLMOps, Go, vector databases, Classical Machine Learning, AutoGen, LangGraph

Natural Language Processing, Sql, Deep Learning, Tensorflow, Big Data Technologies, Machine Learning Algorithms, Rest Apis, Solution Architecture, Python, Langchain, Cloud Platforms, Data Analysis

Sql, MLops, Pytorch, Nosql, Gcp, Logging, AWS, Tensorflow, Python, Azure, RAG pipeline design, LLMs, LlamaIndex, Monitoring, Vector databases, Data Analysis, ML systems, Weaviate, Hyper-parameter tuning, Feature engineering, LangChain, Pinecone, Prompt engineering, Milvus, Model deployment, GenAI applications, Evaluation, FAISS, Sci-kit, Statistical model training

Tensorflow, Machine Learning, Pytorch, FastAPI, Python, Kubernetes, Docker, Flask, Nlp, Azure ML Studio, GitHub Actions, Airflow, MLflow, Kubeflow, GenAI, Hugging Face, Scikit-learn, AWS SageMaker, GCP Vertex AI

AWS, Python, Google Cloud, Microsoft Azure, Information Retrieval using RAG, Vector DBs, LLMs, Fine-tuning, Prompting, Architectural Design for Chatbots

Azure, AWS, Pytorch, Tensorflow, Python, Gcp, Machine Learning, MLops, Conversational AI, Hugging Face, Generative AI

Machine learning (ML) SoCs, Soc Architecture, ASIC design flows, Power/performance/area tradeoffs, RTL integration, Hardware/software interface optimization