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AI Engineer with 1+ years in LLM & RAG Systems
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Assessing your cultural and operational fit
GenAI engineer who ships. In 2+ years I've taken LLM and RAG systems from prototype to production – deploying models on constrained hardware, cutting inference latency, and architecting multi-tenant retrieval that scales to enterprise users. I move fast, measure everything, and care most about whether the thing actually works in production.
Sri Shakthi Institute of Engineering and Technology
B.TECH. · Artificial Intelligence & Machine Learning
N/A – June 30, 2026
Shamlatech Solutions
AI Engineer
August 1, 2025 – Present
Coimbatore, Tamil Nadu, India
Claysys Technologies
AI/ML Engineer
June 1, 2024 – June 1, 2025
Coimbatore, Tamil Nadu, India
Building with the Claude API
Anthropic
June 1, 2026 – Present
Machine Learning Specialization
Stanford University (Coursera)
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience across two companies, focusing on AI Engineer and AI/ML Engineer roles, shows consistent dedication to the field. The projects involve diverse aspects of GenAI, from RAG system development to LLM optimization and multimodal integration, indicating adaptability and a broad interest within AI. The certifications from Stanford and Anthropic further demonstrate a commitment to continuous learning and staying current with industry trends, which aligns well with a dynamic and innovative team culture.
Soft Skills & Operational Fit
The candidate's professional summary highlights a results-oriented approach, emphasizing shipping production systems, measuring performance, and ensuring real-world functionality. This indicates a strong operational fit and a pragmatic mindset, crucial for senior engineering roles. The detailed descriptions of impact (e.g., 'reduced LLM memory footprint by ~20%', 'improved retrieval efficiency by ~60%') suggest a data-driven and performance-focused individual.