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Tier-1 brand, mid-level experience, and popular GenAI skills increase applicant competition.
Core Python, RAG, and agentic AI skills are broadly transferable across industries.
Explicit 3–6 year requirement and many mandatory GenAI skills increase filter strictness.
Assist in building and integrating generative AI applications using pre-trained and hosted foundation models with managed GenAI APIs and open-model endpoints.
Support implementation of context engineering and prompt engineering workflows to create reliable, token-efficient prompts and AI-powered features.
Contribute to development and maintenance of Retrieval-Augmented Generation (RAG), knowledge graph pipelines, agentic workflows, and related AI application deployment and monitoring.
Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
3–6 years professional experience in software/AI development with exposure to Generative AI and agentic AI.
Proficiency in Python and practical experience with RAG systems, prompt engineering, and GenAI API consumption (e.g., OpenAI, Gemini, Claude).
Familiarity with AI deployment tools like Docker and version control with Git; explicit exposure to context engineering and agentic AI tools/protocols (e.g., Google ADK, LangGraph) required.
Experienced working in collaborative AI development teams contributing to practical GenAI or AI projects involving pre-trained foundation models rather than developing new models.
Comfortable applying established GenAI integration patterns including context engineering, RAG, prompt engineering, and agentic workflows under guidance, progressing towards independent ownership.
Familiar with modern AI tooling, frameworks, and protocols supporting agentic and retrieval-augmented AI capabilities in production environments.