Python, Generative AI models, Software engineering principles

Python Programming, metrics, evaluation datasets, fine-tuning, LLM and AI fundamentals, Analytical Skills, AI agent architecture, prompt optimization, production ML AI systems, structured testing, benchmarks, model and LLM evaluation, root-cause analysis, model improvement

Pytorch, Tensorflow, Python, NLP techniques and libraries, Basic MLOps practices, Azure ML or equivalent cloud ML services

Machine Learning, Natural Language Processing, Deep Learning, Numpy, Pytorch, Pandas, Python, Hugging Face Transformers, TesserAct, Scikit-Learn, LayoutLM, PaddleOCR

data engineering , .NET, production deployment , Java, Scalability, Data Science, Cloud, System Design, Backend Engineering, Integration, Python, ML Engineering, Generative AI, LLMs, Agentic AI, Security, RAG, architecture, AI frameworks, AI application development

BigQuery, Dataproc, Sql, Jenkins, Cloud Storage, Terraform, Sonarqube, DataFlow, Python, Logging, Cloud Functions, Cloud Build, Cloud Monitoring, GitHub Enterprise, Pub Sub, GitHub Actions, Cloud Run, Dataplex, Vertex AI

Django, Git, Docker, FastAPI, Rest Apis, Python, Microservices, PostgreSQL, Prometheus, Kafka, Grafana, Datadog, Sql, Nosql, Gcp, MySQL, MongoDB, Splunk, Azure, Kubernetes, AWS, Generative AI, LLMs, CI CD automation, AI ML concepts, RAG, Event-driven architectures