Machine Learning, Apis, Kafka, Microservices, Tensorflow, Git, Pytorch, Gcp, Docker, Opencv, Spark, Databricks, Azure, Kubernetes, Python, AWS, DevOps MLOps practices, Airflow, data pipelines, MLflow, ONNX, AI-assisted engineering tools, CI CD pipelines

pruning , Hive, Python, XGBoost, Spark, feature engineering, monitoring ML LLM systems, scikit-learn, modern LLM tooling, large-scale data pipelines, LangChain, UDFs, feature selection

Python, GenerativeAI, LLM RAG

LLMs (OpenAI, Data Processing & Feature Engineering, Cloud Platforms (AWS/Azure/GCP – optional), Python, Pytorch, LangChain, NLP Concepts

Genai, Prompt engineering, Llm

Python, Sql, tree-based models, R, observability monitoring tools, Generative AI tooling, time-series forecasting, classical ML modeling

Python, AI/ML, AI/ML Engineer, Cloud AI

AI ML, Pytorch, Sql, Nlp, aws, Model Deployment

Microservices, Terraform, Python, AWS, Rust, Cloudformation, Devops, Jenkins, Git, Gcp, FastAPI, Azure, Kubernetes, RAG systems, Hugging Face Transformers, Go, MLflow, TF Serving, vector database management, GitHub Actions, TorchServe, LangChain, big data processing using Spark, vLLM, test-driven development, semantic search implementation, model quantization techniques, CI CD pipelines

Python, image processing techniques, embeddings, Generative AI, LLMs, Azure OpenAI Service, vector databases, deep learning architectures, conversational AI systems, agent workflows, chatbots, AI pipelines, neural network frameworks, RAG pipelines, retrieval pipelines