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AI Engineer with 1+ years in Agentic AI & LLM Infrastructure
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Assessing your cultural and operational fit
Final-year AI/ML engineer specializing in Agentic AI, autonomous multi-agent systems, LLM infrastructure, and multimodal RAG architectures focused on enterprise AI reliability and adaptive reasoning.
Acharya Nagarjuna Univ.
B.Tech · AI & ML
August 1, 2022 – June 30, 2026
Narayana Jr. College
Intermediate · MPC
N/A – Present
Governed Self-Evolving Multi-Agent AI Memory Operating System
January 1, 2025 – June 1, 2026
Architected a governed multi-agent AI operating system enabling persistent memory and safe self-evolving intelligence. Designed hardware-adaptive inference pipelines with dynamic model routing and hybrid memory orchestration. Implemented governance validation, reflection loops, and observability-driven self-improvement workflows.
Adaptive Multi-Agent Self-Healing RAG System
January 1, 2025 – June 1, 2026
Built a cognitive self-healing RAG architecture using adaptive reasoning and hallucination-aware retrieval validation. Designed Retriever, Generator, Skeptic, Fact-Checker, and Judge agents enabling trusted response synthesis. Implemented retrieval-repair pipelines with grounding verification and multimodal retrieval.
Enterprise LLM Benchmarking & Prompt Optimization Framework
January 1, 2025 – June 1, 2026
Designed a benchmarking framework evaluating frontier LLMs across reasoning and hallucination resistance. Engineered prompt evaluation pipelines analyzing factual grounding and response consistency.
Cultural Fit Analysis
The candidate's projects show a strong alignment with cutting-edge AI research and development, particularly in Agentic AI and LLM infrastructure, which aligns well with an AI Engineer role. Their interest in frontier AI labs and Indian AI startups indicates a desire to work in innovative environments. However, the lack of diverse project types (all academic) and professional experience limits the assessment of broader cultural fit and adaptability to different organizational structures.
Soft Skills & Operational Fit
The candidate demonstrates strong systems thinking, curiosity, and a proactive approach to learning frontier AI research. Their academic leadership experience suggests an ability to drive projects from design to deployment. However, without professional experience, their operational fit in a fast-paced industry setting, including collaboration and stress handling, is yet to be validated.