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Tier-1 brand, metro location, and mid-level role increase applicant density, though GenAI specialization narrows the pool.
GenAI engineering skills are transferable, though audit-specific compliance needs moderate domain familiarity.
Multiple mandatory GenAI, LLM, RAG, vector DB, cloud, and compliance requirements increase screening rigor.
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Design, develop, and deploy enterprise-grade Generative AI solutions using Large Language Models (LLMs).
Build and optimize Retrieval-Augmented Generation (RAG) pipelines and embedding-based vector search systems.
Collaborate across teams to identify AI-driven business use cases and ensure scalability, reliability, and performance of GenAI applications.
Graduate degree in B.Sc/B.Tech in Artificial Intelligence, Data Science, or a related field.
Experience with Generative AI frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel).
Hands-on experience with RAG architectures, embedding-based search, Prompt Engineering, and LLM deployment.
Familiarity with cloud AI platforms like Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Experienced in working with various LLMs such as GPT, Claude, Gemini, Llama, or Mistral, including advanced integration and deployment.
Proficient with AI-assisted coding tools and development environments (VS Code, IntelliJ with GitHub Copilot).
Able to address AI solution compliance aspects like Data Privacy, Security, Access Control, and cost optimization strategies for LLM usage.