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High due to Tier‑1 brand, mid-level GenAI role, metro location, and broad candidate pool.
Medium because core GenAI engineering skills transfer, but agentic and compliance exposure adds specificity.
High due to explicit 4+ years requirement and mandatory GenAI, RAG, embeddings, and Python skills.
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Develop and integrate generative AI applications using pre-trained foundation models, focusing on retrieval-augmented generation (RAG), prompt engineering, and agentic workflows.
Take ownership of well-scoped AI components, collaborating with senior developers to build token-efficient, context-aware AI solutions that are reliable and production-ready.
Support deployment, monitoring, and maintenance of GenAI applications while contributing to data preprocessing, API development, and integration with data scientists and engineers.
Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field.
At least 4 years total experience with 2-4 years professional experience in software or AI development, including Generative AI and agentic AI exposure.
Proficiency in Python and hands-on experience with prompt engineering, RAG systems (chunking, embedding, semantic search), and knowledge of agentic AI frameworks (e.g., Google ADK, LangGraph).
Experience consuming major GenAI APIs and understanding of application deployment and containerization (Docker).
Experience operating in collaborative team settings with exposure to AI/GenAI software projects and cloud AI/ML environments (AWS, GCP, Azure) as a plus.
Capability to progressively deepen technical expertise by applying established engineering patterns under senior developer guidance.
Focused on building scalable, context-aware, and traceable AI features within controlled, well-scoped components using modern GenAI stacks and agentic protocols.