production deployment , Tensorflow, Pytorch, Python, Version Control, Application Architecture, System Design, Containers, orchestration technologies, AI ML applications, CI CD, observability tools, code review practices, evaluation frameworks, LangChain, operational monitoring, Documentation, deep learning frameworks, retrieval-augmented generation, Semantic Kernel, Production Support, agent development patterns, performance optimization, model APIs, cloud ML platforms, prompt orchestration, LlamaIndex

data engineering , Machine Learning, Deep Learning, Python, Tensorflow, Cloud Computing

Natural Language Processing, Computer Vision, Deep Learning, Tensorflow, Machine Learning, Pytorch, Autoencoders, Python, Fine-tuning mechanisms, diffusion, Multi-task learning, Generative modeling techniques, Adapter training, Transformer architectures, Hyperparameter Optimization, Generative AI

Java, Docker, Node.js, Kubernetes, Python, DevOps practices and tools, Prompt engineering, Generative AI or LLM-based solutions

System Design, Application Development, Testing, Python, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems

Sql, MLops, Gcp, Azure, Python, AWS, data pipelines, AI system design, LLMs, Cloud platforms, end-to-end AI solution development, CI-CD practices, RAG

snowflake , Sql, Amazon CloudWatch, AWS, Python, Docker, Git, LangSmith, API frameworks, Langfuse, MLflow, LLM systems, Snowpark, Snowflake Cortex, OpenTelemetry, Amazon Bedrock, AgentCore, LangChain, LangGraph

New Relic, Github, Azure Functions, Datadog, FastAPI, Python, Docker, Azure DevOps, Application Insights, Azure Monitor, Semantic Kernel, Azure OpenAI, OpenTelemetry, Azure Container Apps, Weaviate, AutoGen, LangChain, Azure AI Search, Pinecone, FAISS, Azure AI Studio

Docker, MLops, Etl, AWS, Python, Kubernetes, Azure, Gcp, Nlp, LLMs, Transformers, feature store, vector DBs, embeddings, data pipelines, RAG, prompt engineering