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Generative AI Engineer with less than a year in RAG, LLMs & Multi-Agent Systems
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Entry-level Generative AI Engineer with hands-on experience building Retrieval-Augmented Generation (RAG) systems, advanced RAG pipelines (query rewriting, hybrid search, re-ranking), and tool-using LLM applications via the Model Context Protocol (MCP). Skilled in Python, LangChain, vector databases, and multi-agent orchestration, with a track record of shipping production-style GenAI prototypes including market research agents, multi-agent pipelines, and FastAPI-based LLM services.
Cultural Fit Analysis
The candidate's projects demonstrate a strong focus on cutting-edge Generative AI technologies, aligning well with a Generative AI Engineer role. The diversity of projects (RAG pipelines, multi-agent orchestration) and the breadth of skills listed (various LLM frameworks, agentic tools, backend development) indicate a proactive learning attitude and adaptability. The M.Sc. in AI/ML further reinforces a strong interest and commitment to the field. The previous experience as a CIS Executive, while not directly AI-related, shows a background in technical operations and system management, which can be beneficial for understanding deployment and infrastructure considerations.
Soft Skills & Operational Fit
The resume highlights project-based work demonstrating problem-solving and implementation skills. However, there is no direct data to assess soft skills like teamwork, leadership, or communication style in a collaborative environment. The previous role as CIS Executive suggests operational reliability and system management, which could translate to attention to detail and structured thinking.