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Strong employer brand plus Bangalore metro increases applicant density but role is specialized.
Specialized LLM, RAG, and vector DB expertise reduces cross-industry transferability.
Explicit 8-12 years and numerous mandatory LLM, RAG, and cloud skills imply stringent filters.
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Design, develop, and deploy Generative AI solutions using Large Language Models (LLMs) including building and optimizing Retrieval-Augmented Generation (RAG) pipelines.
Develop embedding-based search and retrieval systems with vector databases and create/refine prompts to improve AI model performance and accuracy.
Integrate LLMs via APIs and AI frameworks for production applications ensuring scalability, reliability, and collaborating with cross-functional teams on AI-driven business solutions.
Bachelor’s degree in Artificial Intelligence, Data Science, or related field (B.Sc/B.Tech or equivalent).
8-12 years of work experience in AI engineering with hands-on expertise in Generative AI and LLMs.
Experience with GenAI frameworks like LangChain, LlamaIndex, Semantic Kernel and cloud AI platforms like Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Proficiency in RAG architectures, embeddings, vector stores, prompt engineering, LLM deployment, and knowledge of data privacy, access control, and cost optimization in AI solutions.
Senior-level AI engineer with extensive practical experience in LLM-based product development and deployment at scale.
Experienced in operationalizing advanced GenAI systems using RAG, prompt engineering, vector databases, and cloud AI platforms.
Capable of managing end-to-end AI solution delivery including model integration, performance optimization, security/compliance, and cross-team collaboration in enterprise settings.