Back
Tier-1 employer, metro location, and a visible AI Engineer title increase applicant competition despite niche LLM requirements.
LLM, RAG, and vector-search skills translate across industries, though controls and compliance needs add moderate specificity.
Extensive mandatory GenAI, LLM, RAG, vector store, and cloud-platform skills make filtering highly rigorous.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy Generative AI solutions using Large Language Models (LLMs).
Build and optimize Retrieval-Augmented Generation (RAG) pipelines and embedding-based search systems with vector databases.
Integrate LLMs via APIs and AI frameworks ensuring scalability, reliability, and performance of GenAI applications.
Graduate degree in Artificial Intelligence, Data Science, or related field (B.Sc/B.Tech or equivalent).
Experience with GenAI frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
Hands-on expertise in Retrieval-Augmented Generation (RAG), embeddings, vector stores, semantic search, and prompt engineering.
Familiarity with cloud AI platforms like Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Demonstrated ability to manage full lifecycle of LLM-based AI product from design through deployment in enterprise settings.
Strong technical expertise in large language model integration, prompt engineering, and vector database utilization for search and retrieval.
Experienced working within multi-disciplinary teams to translate business requirements into AI solutions with considerations for data privacy, security, and cost optimization.