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Strong employer brand, metro location, and mid-level experience increase applicant competition, despite GenAI specialization.
Core GenAI, LLM, and Python skills are highly transferable across industries.
Explicit years, mandatory GenAI experience, and specific LLM/RAG toolset enforce strict filtering.
Design, develop, and deploy production-ready Generative AI (GenAI) solutions using Retrieval-Augmented Generation (RAG), large language models (LLMs), and multi-agent frameworks.
Build and scale RAG systems with integration of graph-based memory and tool-using agents enhancing LLM capabilities.
Collaborate across teams to translate business requirements into scalable, reliable, and maintainable GenAI-powered systems following strong software engineering practices.
2 to 7 years total experience in AI/ML or software engineering with minimum 2 years hands-on experience in building and deploying Generative AI systems.
Proficiency in Python, experience with LLMs such as GPT-4, Claude 2, Gemini, and knowledge of RAG systems and multi-agent frameworks (LangChain, AutoGen, LangGraph).
Bachelor's degree in Engineering, MCA, MTech, or MBA (Master of Business Administration).
Experience or familiarity with cloud-native deployments (APIs, containers, microservices) and strong software engineering best practices (version control, testing, CI/CD).
Experienced AI Engineer focused on building scalable production-grade GenAI solutions with deep expertise in agentic AI and multi-agent orchestration frameworks.
Strong software engineering discipline with an emphasis on maintainability, scalability, and cross-functional collaboration.
Comfortable working with advanced AI tooling and cloud-based LLM platforms, able to integrate cutting-edge GenAI technologies into enterprise solutions.