MCP (Model Context Protocol), Tcp, PostgreSQL, Containers, Concurrency, Ruby, Tls, Kubernetes, Anthropic APIs, Go, Security, HTTP 1.1, Deployment pipelines, JSON streaming, HTTP 2

Java, Sql, MLops, Python, LangChain, LLaMA, Data preprocessing, LLMOps, Claude, Pinecone, vLLM, Feature engineering, LMDeploy, LangGraph, FAISS, Milvus, OpenAI, Gemini, Weaviate, Model evaluation

Spark SQL, Tensorflow, Pytorch, Linux, Apache Kafka, Databricks, Api, Keras, Python, Gemini API, Ai, OpenAI API, Apache Parquet, AI Evaluation, low latency, Responsible AI, Claude API, Gemini, Model Quantisation, Prompt Injection

Tcp, PostgreSQL, Containers, Json, Http, Concurrency, Ruby, Tls, Kubernetes, Profiling, Go, Observability

Prometheus, Grafana, Kubernetes, Python, RDMA, GitOps, Go, NVSwitch, Volcano, NVIDIA NVL72 GB300, OpenTelemetry, KAI, NVLink, GPU scheduling, YuniKorn, Kueue, Slurm

Tensorflow, Cloud Services, Pytorch, Python, machine learning frameworks, AI-based video processing, DevOps practices, Azure AI, video analytics

Phoenix, Datadog, Python, LLM-based systems, OpenTelemetry, Langfuse, evaluation frameworks, multi-agent environment, Arize

data engineering , ML Libraries (scikit-learn), Version Control (Git), PyTorch), Containerization (Docker), Deep Learning Frameworks (TensorFlow, Data Processing (pandas, Orchestration (Kubernetes), NumPy), Java, Spring Boot, Python, Machine Learning, Distributed Systems, Artificial Intelligence, Restful Services, Agentic AI, Microservices Architecture, API Design Principles, Event-driven Architectures

Machine Learning, Distributed Systems, Saas, tool integration, retrieval-based architectures, orchestration technologies, modern LLMs, prompt optimisation, Ai, AI agents, architectural standards