Machine Learning, Artificial Intelligence, Prometheus, Grafana, Sql, MLops, Docker, Azure, Kubernetes, Python, AWS, Generative AI, LLMOps, Data Bricks, Agentic AI, Prompt Engineering, Large Language Models

Databases, Apis, Logging, Cloud deployment, Automated Testing, Git, Enterprise Security, Containers, Python, retrieval tool calling workflow orchestration, identity secrets management, CI CD, alerting, Incident Investigation, event-driven integrations, access controls, human-in-the-loop processes, performance optimization, Production Monitoring

Sql, Tensorflow, Pandas, Pytorch, Spark, Splunk, Python, Monitoring for data and model drift, Scikit-learn, MLflow, Model versioning, Flink, LLM-based agents, Agentic AI, CI/CD for ML, Ciena Blue Planet UAA

Github, Apis, Tensorflow, Numpy, Git, Pandas, MLops, Pytorch, Docker, Azure, Kubernetes, Python, AWS, agentic architecture, GenAI, scikit-learn, LLMOps, Llm, distributed services, infrastructure as code

graph databases , probability , Machine Learning, Cloud Security, Sql, Deep Learning, Cuda, Tensorflow, MLops, Pytorch, Terraform, Docker, Python, Kubernetes, Generative AI, Vector databases, MLflow, calculus, Kubeflow, Econometrics

Machine Learning, Apis, Natural Language Processing, Microservices, Devops, Data Privacy, Python, CI CD, cloud environments, responsible AI, AI services, Security, model-serving pipelines, large language models, data ingestion

data engineering , .NET, production deployment , Java, Scalability, Data Science, Cloud, System Design, Backend Engineering, Integration, Python, ML Engineering, Generative AI, LLMs, Agentic AI, Security, RAG, architecture, AI frameworks, AI application development

Apache Spark, Sql, Tensorflow, Pytorch, Databricks, Python, Machine Learning Algorithms, model versioning, deep learning frameworks, Lakehouse architectures, cloud platforms, MLflow, Monitoring, MLOps practices, Statistical Modeling, drift detection, CI CD for ML, automated retraining

Sparql, Python, Ontology mapping, NLP semantic engineering, AI engineering, graph-aware retrieval, RAG GraphRAG implementation, LLM application development, RAG pipeline, Vector store integration, semantic disambiguation