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Tier-1 brand, metro location, and mid-level GenAI role create high applicant competition.
Role requires specialized GenAI and LLM experience, making industry background and skills highly sensitive.
Explicit minimum experience plus specialized GenAI, vector DB, Databricks, and cloud requirements create high strictness.
Design, develop, and deploy enterprise AI applications and Generative AI solutions using Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and knowledge retrieval architectures.
Build scalable Python applications, APIs, microservices, and data engineering pipelines leveraging cloud-native, distributed processing technologies like Databricks and Spark.
Collaborate with technology leaders and stakeholders to deliver secure, production-ready AI solutions, supporting AI adoption and contributing to reusable frameworks and enterprise AI capabilities.
Minimum 2+ years software engineering experience or equivalent via training, education, or military experience.
3-6 years experience preferred in software engineering for enterprise-scale applications, distributed systems, with proficiency in Python and cloud-native development.
Experience building Generative AI/LLM solutions with 1-3 years focused on GPT, LangChain, RAG, embeddings, semantic retrieval, and AI/LLMOps.
Experience with data engineering ETL/ELT pipelines, distributed computing (Spark, Databricks), REST APIs, microservices, and knowledge retrieval technologies (vector DBs, NLP frameworks).
Engineer with 3-6 years experience delivering enterprise AI/GenAI applications using Python and cloud-native platforms like GCP.
Demonstrated strong skills in building scalable, high-performance AI solutions including Generative AI, RAG, Agentic AI, and knowledge retrieval systems.
Experience working cross-functionally with data engineers, architects, and business teams to deploy production-grade AI systems with CI/CD, DevOps, and AI/LLMOps practices.