ML certification (AWS Certified Machine Learning Specialty or equivalent, Containers, Cloudformation, Kubernetes, Terraform, Docker, Amazon SageMaker, Microservices architectures, Amazon Bedrock, Agentic workflows, Infrastructure-as-code, RAG architectures, DevSecOps tooling, AI/ML fundamentals, Prompt engineering, CI/CD pipelines

Python, Microservices, Algorithms, Agile Methodologies, Oracle, Terraform, PostgreSQL, AI ML technologies, LLMs, CI/CD, Gen AI, Java 17+, EC2 instances, Enterprise-authorized AI-assisted software development tools, Core Database concepts, Modern programming languages, Database querying languages, Agentic AI, Security, AWS Elastic Kubernetes Service, Application Resiliency

Agile Methodologies, PostgreSQL, AI ML, Oracle, Python, Microservices, AWS Elastic Kubernetes Service, EC2 instances, Terraform scripts, AWS production environment, CI/CD, Database querying languages, Enterprise-authorized AI-assisted software development tools, Application Resiliency and Security

Nlp, Machine Learning, MLops, Docker, Python, Kubernetes, LangChain, Vertex AI, MLflow, Generative AI LLMs, Azure OpenAI

XGBoost, Feature engineering at scale in Snowflake, Amazon Bedrock foundation-model integration, Agentic AI prompt engineering, Splink probabilistic matching, Hedonic pricing models, LightGBM, Python ML engineering, MLOps model evaluation pipelines, Serving-API development

data engineering , Kubernetes, Big Data Technologies, S3, MLops, Etl, Cloud Services, Python, Docker, EKS, Athena, Infrastructure as Code, Glue, AI-assisted software development tools, Containerized application development