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AI Engineer with 2+ years in Generative AI & LLM Systems.
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Assessing your cultural and operational fit
Machine Learning and Generative AI Engineer with 2 years of experience building LLM-based agent systems, Retrieval-Augmented Generation (RAG) pipelines, and production AI applications. Experienced in end-to-end model lifecycle including retrieval system design, evaluation frameworks, and deployment using Docker and AWS EC2. Strong focus on scalable AI systems, reliability, and experimentation-driven development.
SCSVMV University
Master of Computer Applications (MCA)
N/A – June 30, 2023
Open Source Contributor
AI Systems Engineer
October 1, 2025 – Present
India
HFG Entertainments
Applied AI Engineer
February 1, 2024 – October 1, 2025
India
Autonomous NeuralSpine Multi-Agent Framework
June 25, 2026 – Present
Architected an autonomous multi-agent engineering framework (currently in continuous prototyping stage) that executes zero-click file debugging, self-healing iteration loops, and plain-English project scaffolding. Developed a programmatic 8-signal reward engine that computes real-time syntax passing, runtime sandboxing execution, structural AST retention, and token constraint validations to grade and guide model mutations. Designed a cross-platform model orchestration for quantized GGUF inference, complete with long-term pattern recall memory systems and a graceful fallback configuration.
View ProjectRAG AI Agent
June 25, 2026 – Present
Engineered an automated system orchestration pipeline that parses unstructured EDA text reports into structured schema matrices to automatically map optimal Scikit-Learn data transformations. Optimized a two-stage RAG pipeline utilizing a local FAISS vector index and BGE embeddings, featuring a custom deterministic re-ranking algorithm to enforce rigorous domain filtering over dense retrieval outputs. Architected a 5-stage validation and critic engine integrating regex pattern safety scrubs, categorical type-enforcement layers, and a secondary LLM verification loop to eliminate model hallucinations.
View ProjectCultural Fit Analysis
The candidate's involvement in open-source contributions and diverse project portfolio (RAG agents, multi-agent frameworks, game AI) demonstrates adaptability and a proactive learning attitude. The focus on cutting-edge AI technologies and system-level thinking suggests a good fit for an innovative and technically driven environment. The master's degree in Computer Applications further supports a strong academic foundation.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and an ability to architect complex systems. The open-source contributions suggest initiative and a collaborative mindset. The focus on optimization and efficiency in past roles aligns well with operational excellence.