Deep Learning, Git, Computer Vision, Python, real-time video streams, model optimization techniques, data preparation or processing pipelines, large-scale image and video datasets

Machine Learning, Tensorflow, Pytorch, Docker, automation, Python, AWS, Git, MLops, Gcp, Azure, Kubernetes, Airflow, Infrastructure as Code, model versioning, MLflow, Kubeflow, model registries, feature stores, DevOps practices, Scikit-learn, Ai, Monitoring, CI CD pipelines, ML model deployment

Docker, Pyspark, Pytest, Azure Databricks, Azure Cloud Services, Python, Sql, MLflow

Java, Machine Learning, Distributed Systems, Hadoop, Statistical Modelling, Sql, API Design, MLops, Spark, Python, Scalable Architectures, Large-scale Data Systems, Go, Optimisation Techniques, Scientific Computing

Spark, Java, Hadoop, Cloudformation, Yarn, Tensorflow, Hive, MLops, Docker, Sns, Python, Kubernetes, AWS, Airflow, Pipelines, H2O, EventBridge, CodeBuild, SageMaker, CodePipeline, CodeCommit, CloudTrail

Prometheus, Gpu, Grafana, Pytorch, Docker, Python, Linux, Kubernetes, embeddings, ML Engineering, tool calling, Hugging Face, LLM Inference, SGLang, LangSmith, Langfuse, RAG LangGraph, quantization, Observability, TensorRT-LLM, KV Cache, vLLM, Agentic AI, TGI, LoRA, RAG, W B, LlamaIndex, QLoRA, AI Infrastructure

Terraform, Docker, Prometheus, Postgres, Gitlab, Grafana, Kubernetes, Python, AWS, LLMs, MLFlow

Numpy, Machine Learning, Pandas, Python, MATLAB, Scikit-Learn

C Language, R Programming, Python, Unix, Linux, Artificial Intelligence and Machine Learning, Llm