containerization , Java, Test Automation, C#, Distributed Systems, Software Architecture, Agile Development Methodologies, Javascript, Python, Modern Development Practices, CI/CD Pipelines, DevOps Practices, Microservices Architecture, Application Engineering

Neo4j, AI ML, MLops, MongoDB, production-grade reliability, AWS Bedrock, re-ranking, vector databases, responsible AI controls, LangGraph, AWS Neptune, embedding, chunking, output evaluation, document pipelines, DORA metrics, engineering standards, similarity thresholds, LangChain, RAG architecture, LLMOps, managed inference infrastructure, hallucination detection, SageMaker, LLM orchestration frameworks, prompt tracing, data-driven model evaluation, AI observability, knowledge graph architectures

Ml, Apis, cloud security, Vulnerability Management, Cybersecurity, Devops, Software Architecture, Application Security, Gcp, Cloud Infrastructure, Azure, Python, AWS, Generative AI, LLMs, Ai, secure software development, RAG AI agents, CI CD practices

containerization , Sql, Sdlc, Kafka, Cloud Technologies, Kubernetes, Data Modelling, Nosql, Restful Apis, agentic AI systems, messaging systems, AI ML technologies, LLMs, data stores, microservices development, Production Operations, CI CD, observability, modern deployment practices

MLops, Neo4j, AI ML, MongoDB, embedding, eval-driven development, AI observability, RAG architecture, document pipelines, LLMOps, Cloud cost optimisation, re-ranking, DORA metrics, LangChain, AWS Bedrock, data-driven model evaluation, responsible AI controls, LangGraph, production-grade reliability, chunking, engineering standards, vector databases, SageMaker, managed inference infrastructure, similarity thresholds, prompt tracing, output evaluation, hallucination detection, prompt discipline, AWS Neptune

Pytorch, Tensorflow, Azure ML, Deep Learning, MLops, Machine Learning, AWS SageMaker, Google AI, computer science fundamentals, LLM fine-tuning, training methodologies, Scikit-learn