Azure, Ml, Java, MLops, Gcp, Docker, Kubernetes, Python, Microservices, AWS, Ai

Software Architecture, Artificial Intelligence, Deep Learning, Python, Design Patterns, Machine Learning, high-performance computing

Java, Tensorflow, Pytorch, Docker, Distributed Systems, Python, Kubernetes, LLMs, Go, vector databases, model serving infrastructure, microservices architecture, ML pipelines, transformer architectures, RAG, high availability production environments

Data Science, Machine Learning, Nlp, Python, deep learning frameworks, cloud-native, Statistics

MLops, Prometheus, Grafana, Kubernetes, LangChain, Generative AI, Triton Inference Server, AI ML engineering, NVIDIA GPU environments, MLflow

Agile Methodology, PostgreSQL, Prometheus, Grafana, JIRA, React, Openshift, FastAPI, Python, RKE2, Quota billing systems, GPU orchestration, Multi-tenant SaaS architecture, RBAC models, Model Deployment, CI/CD, SSO federation, Observability Metrics, Secrets Management, Kubernetes-native application development, GPU Quota Billing

containerization , Data Modelling, Cloud Technologies, Sql, Sdlc, Nosql, Restful Apis, Kubernetes, LLMs, UI technologies, CI CD, data stores, agentic AI systems, microservices development, Production Operations, messaging systems such as Kafka, AI productivity tools such as Claude or GitHub Copilot, AI ML technologies, modern deployment practices, observability

Python, DevOps CI CD and platform engineering practices, Observability monitoring and evaluation frameworks for AI systems

Gcp, AWS, Metadata Management, Machine Learning, Data Governance, Lakehouse architecture, Apache Iceberg, Parquet, Ai, Open data standards