LLM with AWS Professional

Key
Design, build, and deploy AI-driven applications using Generative AI, LLMs, and Agentic AI frameworks. Implement RAG pipelines, including prompt engineering, vector databases, and document retrieval systems. Develop and fine-tune models using frameworks such as TensorFlow, PyTorch, or Hugging Face. Integrate external AI models and APIs (e.g., OpenAI, Anthropic, AWS Bedrock) into existing systems. Build and manage data ingestion, processing, and orchestration pipelines using AWS Glue, Lambda, and Airflow. Work with AWS services such as S3, SageMaker, Athena, EC2, ECS, SNS, Cloud Watch, Glue, Step Functions, Comprehend, and Macie to support AI workflows. Implement and manage vector databases (e.g., FAISS, Pinecone, or ChromaDB) for retrieval-based AI systems. Collaborate cross-functionally with data engineers and scientists to design scalable model training, evaluation, and deployment workflows. Develop automation scripts and tools in Python and PySpark for data analysis, model evaluation, and pipeline optimization. Ensure robust model evaluation, performance monitoring, and compliance with ethical AI principles. Required Skills Experience Hands-on experience with AI-based development using LLMs, RAG architectures, and Generative AI frameworks. Proficiency in LangChain, Haystack, Llama Index, and Hugging Face Transformers. Deep understanding of ML algorithms, deep learning concepts, and model fine-tuning techniques. Experience implementing Agentic AI systems and multi-agent workflows. Strong programming skills in Python, with working knowledge of TensorFlow, PyTorch, or similar frameworks. Solid experience with AWS Bedrock for deploying and scaling generative AI workloads. Proficiency in AWS data services S3, Glue, Athena, Lambda, SageMaker, ECS, Comprehend, Macie. Experience with data engineering tools such as Airflow and PySpark. Strong knowledge of SQL for data querying, transformation, and analysis. Familiarity with MLOps principles, model evaluation metrics, and deployment best practices. Ability to thrive in a fast-paced, agile environment and deliver high-quality, production-ready solutions. Preferred Qualifications Experience working with multi-cloud or hybrid AI architectures. Exposure to Ethical AI frameworks and responsible AI development practices. Certification in AWS Machine Learning or Data Analytics (Optional) Prior experience in healthcare, finance, or regulated industries is a plus.