Apis, Microservices, system integration, MLops, Microsoft Azure, AWS, cloud-native solution development, continuous integration for AI models, AWS Developer CLI, Agentic AI solutions, GitHub Copilot, AI-assisted coding tools, vibe coding environments

Data Factory, Emr, Data Warehousing, Tensorflow, DataFlow, Deep Learning, Etl, Pytorch, Gcp, ELT, S3, Apache Spark, AWS, Python, Azure, Nlp, Generative AI, LLMs, HDInsight, LlamaIndex, Hugging Face, LangChain, Pinecone, ChromaDB, RAG architectures, FAISS

object detection , Machine Learning, Artificial Intelligence, Tensorflow, Numpy, Git, Pandas, Pytorch, Opencv, Image Processing, Python, Computer Vision, Segmentation, CNNs, Deep Learning Architectures, Vision Transformers

containerisation with Docker, secrets management, pipeline automation with Azure DevOps, networking and load balancing, relational and non-relational databases, developing production-grade backend services using Python, asynchronous programming frameworks, cloud provisioning frameworks such as Terraform or CloudFormation, infrastructure-as-code, LLM API gateway technologies, container orchestration such as ECS Fargate, identity and access management technologies, observability and monitoring tools, multi-provider integration across major cloud AI platforms

MySQL, PostgreSQL, MongoDB, FastAPI, Kubernetes, Python, LangChain, LangGraph, LLM fine-tuning, RAG pipelines

.NET, Angular, Apis, AI integration

FastAPI, Kubernetes, Python, Docker, PostgreSQL, agent memory systems, context management strategies, platform-level engineering standards, Ragas, prompt lifecycle management, LLM engineering, Azure OpenAI, RLHF, RAG pipeline architecture, LLM evaluation and observability practices, LangChain, pgvector, financial close Record-to-Report accounting, model fine-tuning, LangGraph

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