Machine Learning, Sql, Gcp, MLops, Azure, Python, AWS, Data lakes, LLM ecosystems, GenAI, Deep Learning frameworks, AI deployment frameworks, R, AI governance

Tensorflow, Nlp, Pytorch, Python, Matching reconciliation algorithms, LLM-based systems, Classification, Entity extraction, Production AI deployment, ML frameworks

Python, Agentic AI, Multi-Agent Systems, Generative AI, LLMs, Prompt Engineering, Reasoning Frameworks, RAG, Retrieval-Augmented Generation

Java, Nlp, Git, Python, AI Architecture, embeddings, vector search, RAG patterns, AI Fundamentals, Evaluation, cloud-based delivery, retrieval, NLU

Ml, Nlp, MLops, LangChain, embeddings, LLMOps, GenAI ecosystems, Hugging Face, vector databases, enterprise-scale AI platforms on cloud, open-source models like LLaMA, Mistral, risk and compliance in BFSI, LLM frameworks, AI governance, OpenAI, LlamaIndex, RAG architectures

Terraform, Python, AWS CDK, Amazon OpenSearch Service, Amazon Bedrock

scalable systems , containerization , Machine Learning, Tensorflow, MLops, Pytorch, Terraform, Kubernetes, Python, LangChain, GenAI, Generative AI, Model Routing, LLMs, RAG systems, LLMOps, Hugging Face, ML libraries, transformer architectures, LlamaIndex, Agent Context Management, cloud platforms, Observability Logging, infrastructure as code

.NET, data engineering , DataBricks, Apis, Trello, Predictive Analytics, Jira, Microservices, Computer Vision, Agile Methodologies, Python, Azure AI Foundry, AI GenAI technologies, Ollama, cloud architecture principles

Data Science, Jira, MLops, Databricks, Model deployment, LLMs, Vector databases, MLflow, Guardrails, ML architecture, ML pipelines, AI governance, Vector retrieval, Fivetran, Feature data workflows, Observability, Inference architectures, Monitoring, RAG