Golang, Jax, Prometheus, Cloudwatch, Grafana, Tensorflow, Pytorch, Python, Java, Scala, MLops, Spark, Keras, Kubernetes, Scalable ML architectures, End-to-end machine learning pipelines, Automated remediation, MLflow, Prediction and drift monitoring, Model hosting, Stream processing pipelines, Big data frameworks, Model versioning, API performance monitoring, Embedding scaling, Feature stores, Data versioning

data engineering , Numpy, Pandas, Pytorch, Python, scikit-learn, ML pipelines

Tensorflow, Machine Learning, Pytorch, MLops, Natural Language Processing, Python, Sql, Deep Learning, Vector Databases, Generative AI, Retrieval-Augmented Generation

error analysis , Deduplication, Pytorch, Python, Cloud Storage, Statistical Analysis, data augmentation, model training, model-based labeling, distributed compute, Active Learning, VLMs, Hugging Face LLMs, ablation studies, dataset curation, benchmark evaluation, workflow orchestration, weak supervision, Batch Processing, inference evaluation, synthetic-data generation, Production Monitoring, data-quality measurement, diffusion models, multimodal models

containerization , Github, Unit Testing, Tensorflow, Numpy, Docker, Python, Scipy, Pandas, Kubernetes, Workflows, Databricks, CI CD Pipelines, scikit-learn, Cluster Creation, Edge Deployment, Azure Cloud Architecture for ML, ACR, Repo Management, Portainer, ML Model Design, Deployment

Git, Docker, Machine Learning Algorithms, Sql, Python, Feature engineering, Model evaluation

Tensorflow, Pytorch, Docker, Python, AWS, Sql, Gcp, MLops, Spark, Azure, Kubernetes, Anthropic Claude, Multi-agent frameworks, Azure OpenAI, Agentic AI systems, Mistral, RAG architectures, LLMs, Prompt engineering strategies, Scikit-learn, model deployment, Fine-tuning pipelines, Agentic AI, Llama, RAG, OpenAI, Gemini

Python, Tensorflow, Pytorch, Docker, Kubernetes, Cloud Platforms