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Generative AI Engineer with 1+ years in Generative AI, MLOps & Hybrid RAG Architectures
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Assessing your cultural and operational fit
Results-driven AI Engineer specializing in Generative AI, multi-agent orchestration, and hybrid RAG architectures. Experienced in building production-grade AI systems using FastAPI and PostgreSQL with a strong focus on scalability, low latency, and reliability. Skilled in designing end-to-end pipelines spanning ingestion, retrieval, validation, and deployment of intelligent systems.
GLA University, Mathura
Bachelor of Technology · Computer Science & Engineering
August 1, 2021 – June 30, 2025
Mobcoder
SDE-1 (AI Engineer)
July 1, 2025 – April 1, 2026
India
S.O. Infotech Pvt. Ltd.
Data Science Intern
May 1, 2023 – December 1, 2023
India
GovTasker - Enterprise Regulatory RAG Platform
June 25, 2026 – Present
Built a production-grade RAG system using LangGraph for compliance-based applications. Designed a hybrid retrieval pipeline enabling accurate clause-level search. Implemented a validation system ensuring grounded and traceable outputs. Developed an asynchronous FastAPI backend with WebSocket streaming and caching.
Sovereign AI – LLM Safety & Observability Platform
June 25, 2026 – Present
Developed a real-time guardrail system for detecting hallucinations and prompt injection. Designed a 3-tier detection pipeline (Regex → Vector Search → LLM) optimized for low latency. Integrated real-time monitoring and observability tooling for system health. Deployed a scalable system using Docker and container-based infrastructure.
Cultural Fit Analysis
The candidate's projects (GovTasker, Sovereign AI) demonstrate an interest in practical, impactful AI applications, aligning well with a product-focused engineering culture. The breadth of skills listed, from backend development to generative AI and retrieval systems, suggests adaptability and a willingness to learn diverse technologies. The focus on building production-grade systems indicates a pragmatic and delivery-oriented mindset.
Soft Skills & Operational Fit
The candidate's project and experience descriptions indicate a results-driven approach with a focus on scalability, low latency, and reliability. The ability to design complex systems like hybrid retrieval pipelines and multi-tier detection pipelines suggests strong problem-solving and analytical skills. The emphasis on monitoring, logging, and caching points to an understanding of operational best practices for AI systems.