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AI Engineer with less than a year in Agentic Systems & RAG Pipelines
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML engineer with production experience across agentic systems, RAG pipelines, multi-agent orchestration, and scalable data infrastructure (1M+ documents, 100k+ businesses). Specialises in Machine Learning, Data Science, LLM integration, prompt engineering, and Software Development. Strong Python foundations backed by a 9.83 CGPA and a national IT award. Equally comfortable owning end-to-end ML system design and integrating AI into existing engineering stacks.
Gujarat Technological University
B.E. · Computer Science (AI & ML)
August 1, 2021 – June 30, 2025
Q3Veni Pipara & Co.
Software Engineer - AI/ML
October 1, 2025 – Present
India
Petpooja
Data Scientist Intern
December 1, 2024 – June 30, 2025
India
Gujarat Technological University
Research Assistant – Embedded ML
June 1, 2023 – November 30, 2023
India
Agentic Conversational Assistant (RAG + LangGraph)
June 24, 2026 – Present
• Orchestrated LangGraph multi-agent graphs with MCP-style tool design, vector memory (FAISS), and structured context window management validated across 50+ multi-turn scenarios with under 2s end-to-end latency per agent hop.
LSTM Stock Price Predictor + Options Pricing
June 24, 2026 – Present
• Built multi-step LSTM forecasting models and Monte Carlo options pricing; deployed via FastAPI + Streamlit for real-time financial inference at sub-500ms response.
CycleGAN Image-to-Image Translation
June 24, 2026 – Present
• Trained on 6,000+ Monet-Photo samples; tuned identity and cycle-consistency losses for high-fidelity style transfer, demonstrating applied deep learning at scale.
Cultural Fit Analysis
The candidate's project diversity, ranging from financial prediction to image-to-image translation and agentic assistants, shows a broad interest and adaptability in various AI/ML domains. Their experience in both personal projects and professional roles (internship, full-time) demonstrates initiative and a commitment to applying theoretical knowledge to real-world problems. The target role of 'AI Engineer' aligns perfectly with their specialized experience in LLM integration, RAG, and multi-agent systems. The breadth of skills across AI/LLM, ML frameworks, languages, and infrastructure indicates a well-rounded technical profile suitable for collaborative and innovative environments.
Soft Skills & Operational Fit
The candidate's project descriptions and experience highlight a strong focus on problem-solving, optimization, and delivering tangible results. The ability to reduce analyst workload by 70% and achieve sub-100ms API latency demonstrates a results-oriented approach. Their work on multi-agent orchestration and fault-tolerant retry logic suggests an understanding of robust system design and operational resilience. The candidate's academic achievements and diverse project portfolio indicate a proactive and continuous learning mindset, which is crucial for an AI Engineer role.