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Senior, niche GenAI architect in Bangalore reduces applicant density despite metro location.
Specialized GenAI and AWS ML experience limits transferability across non-AI industries.
Explicit 12–15 years and mandatory LLM/AWS/SageMaker experience make filters highly stringent.
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Own end-to-end architecture and implementation of cloud-based ML and GenAI solutions on AWS, including model training, deployment, and optimization.
Develop and fine-tune Large Language Models (LLMs) and Generative AI models like LLama2 using AWS services such as SageMaker, Bedrock, and OpenAI APIs.
Leverage technologies such as RAG architecture and vector indexing (Opensearch, Elasticsearch) to build scalable, production-ready ML applications with real-time and batch inference.
12+ years of hands-on experience in machine learning implementation on AWS, including expertise with AWS SageMaker and AWS ML services.
Proven experience working with LLMs, GenAI frameworks (AWS Bedrock, OpenAI), fine-tuning models like LLama2, and RAG architecture using vector databases.
Strong knowledge of deep learning concepts, NLP techniques, transformers (BERT, Attention models), and prompt engineering for LLM optimization.
Experience designing software architecture and integrating workflow orchestration tools such as Airflow, StepFunctions, SageMaker Pipelines, or Kubeflow.
Experienced architect capable of leading complex ML and GenAI projects with native AWS cloud services in a scalable production environment.
Proficient in fine-tuning and evaluating large language models and deploying end-to-end ML pipelines with strong model optimization skills.
Collaborates effectively with cross-functional teams to translate business requirements into robust technical designs and scalable AI applications.