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AI Engineer with less than a year in Multi-Agent Systems & LLM Workflows
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Agentic AI intern-level developer with hands-on exposure to multi-agent systems, LLM application workflows, FastAPI-based backend integration, and retrieval-augmented generation concepts. Currently working in an Agentic AI Developer role at Codage Habitation and building practical knowledge in Python, LangGraph, prompt engineering, and AI automation. Strong fit for internship and entry-level roles focused on AI agents, intelligent workflows, and backend-driven LLM products.
JG University
Master of Computer Applications (MCA) · Computer Science
August 1, 2025 – June 30, 2027
Silver Oak University
Bachelor of Computer Applications (BCA) · Computer Science
August 1, 2023 – June 30, 2025
Codage Habitation
Agentic AI Developer
April 1, 2026 – Present
Ahmedabad, Gujarat, India
Made Multi-Agentic Workflow System for Research Agent
June 24, 2026 – Present
Explored multi-agent workflow patterns for structured task execution, response generation, and tool-oriented reasoning in AI applications. Built understanding of agent coordination, step-based control flow, and backend integration for practical AI automation scenarios.
Agentic RAG Applications
June 24, 2026 – Present
Practiced retrieval-augmented generation concepts to improve contextual grounding and response quality in LLM-based systems. Worked on combining prompt engineering, retrieval logic, and application workflows to support more reliable AI outputs.
Cultural Fit Analysis
The candidate's projects and experience are highly focused on AI engineering, specifically agentic AI, LLMs, and RAG. This specialization indicates a strong alignment with roles centered around these technologies. However, the breadth of projects is limited to two closely related areas, and the experience is primarily from a single, ongoing role. This narrow focus, combined with an 'intern-level' self-description, suggests a developing professional who is still building diverse experiences. The education is ongoing, which further supports the early career stage. While the direct relevance to an AI Engineer role is high, the overall diversity of experience and skills outside of this specific niche is not extensively demonstrated, leading to a moderate cultural fit score.
Soft Skills & Operational Fit
The candidate's resume indicates a focus on practical application and experimentation in AI workflows, suggesting a proactive and hands-on approach. The description of projects and experience highlights an understanding of agent coordination and structured task execution, which are valuable for operational fit in AI development teams. However, without specific psychometric test results, a comprehensive assessment of soft skills like logical reasoning, work attitude, stress handling, and team collaboration is not possible.