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Tier-1 brand, mid-level generalist AI role, metro location, and broad skillset increase competition.
Core AI and data engineering skills transfer well, though enterprise banking compliance adds some domain bias.
Explicit experience range and many mandatory AI, data engineering, and production deployment skills required.
Design, develop, and deploy enterprise AI applications and Generative AI solutions using LLMs, Agentic AI, RAG, and related knowledge retrieval architectures.
Build scalable Python applications, APIs, microservices, and data engineering pipelines leveraging cloud-native and distributed processing technologies.
Contribute to AI/LLMOps frameworks, engineering standards, and collaborate with cross-functional teams to deliver secure, reliable, production-ready AI solutions.
2+ years of software engineering experience or equivalent demonstrated through work experience, training, military experience, or education.
Proficiency in Python development and experience with API and microservice development.
Experience with AI/GenAI technologies including LLM-based applications, Generative AI, and knowledge retrieval systems is strongly implied but specific minimum years not explicitly mandated.
Notice period: Not explicitly mentioned in the JD.
3-6 years of professional experience focused on enterprise-scale software engineering with strong Python and cloud-native development skills.
1-3 years of hands-on experience building Generative AI and LLM-based solutions using frameworks like LangChain, RAG, embeddings, and vector databases.
Experience working on data engineering pipelines and knowledge retrieval systems in production environments, with familiarity in AI/LLMOps and deployment best practices.