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AI Engineer with less than a year in RAG Pipelines & On-device LLMs
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Fresher AI Engineer with hands-on experience building production-grade RAG pipelines, on-device LLMs, and computer vision systems. Proven ability to ship end-to-end AI applications using LangChain, LlamaIndex, FastAPI, and Docker. Interned at Infosys Springboard and Lavendel Consulting. Led a 100+ member technical association as President. Passionate about building real-world GenAI products at scale.
Kangeyam Institute of Technology
B.Tech · Artificial Intelligence and Data Science
August 1, 2022 – June 30, 2026
Navarasam Matric Hr Sec School
HSC
N/A – Present
Navarasam Matric Hr Sec School
SSLC
N/A – Present
Global Knowledge
In-Plant Intern
June 1, 2026 – Present
India
Infosys Springboard
AI Intern
December 1, 2025 – January 1, 2026
India
Lavendel Consulting
AI Intern
May 1, 2025 – August 1, 2025
India
Hybrid RAG AI Assistant
June 1, 2026 – Present
Built a production-grade RAG system using FastAPI + FAISS + Groq (Llama 3.3 70B) for intelligent document question answering, achieving sub-800ms average response latency. Implemented hybrid retrieval combining dense vector search (FAISS) with live web search (DuckDuckGo + Wikipedia) - improving answer accuracy for out-of-context queries by eliminating hallucinations on live data. Designed REST APIs with dynamic query routing and integrated MLflow to track latency, token usage, and response quality metrics across 1,000+ document chunks. Containerized the full stack with Docker and docker-compose; added session-based memory management and automatic document re-indexing on startup.
Aura AID Offline AI Safety App
June 1, 2026 – Present
Engineered a native Android safety application with an on-device LLM (GGUF via RunAnywhere SDK) designed for zero-connectivity emergency scenarios with no cloud dependency and zero data collection. Built Shake-to-SOS and crash detection features for automated emergency response, paired with an offline AI First-Aid guide that runs entirely on-device. Implemented real-time Live Location Sharing and a Safety Escort system for active threat monitoring - privacy-first design with no API keys or external calls.
Multi-User Real-Time Eye Gaze Tracking
June 1, 2026 – Present
Engineered a real-time gaze tracking system achieving stable 28+ FPS across 5 simultaneous users, with unique color-coded labels per user - deployed as a client deliverable at Lavendel Consulting. Improved gaze accuracy by fusing iris tracking, head-pose estimation, and regression models, reducing tracking drift compared to single-model baselines. Designed a modular pipeline supporting concurrent face recognition and calibration per user session.
AI-Powered LinkedIn Automation Bot
June 1, 2026 – Present
Built an LLM-powered content automation pipeline that generated and published professional LinkedIn posts - reducing manual social media effort by 80. Implemented secure OAuth 2.0 authentication for programmatic posting via the LinkedIn API, with structured prompt templates for consistent tone and format.
Artificial Intelligence Primer
Infosys Springboard
June 1, 2026 – Present
Foundation of Cloud, IoT & Edge ML
NPTEL
June 1, 2026 – Present
Prompt Engineering
Infosys Springboard
June 1, 2026 – Present
Joy of Computing Using Python
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for an innovative AI engineering role. Their diverse projects, ranging from RAG systems to on-device AI and computer vision, show a broad interest and capability in various AI domains. The leadership roles in student associations and participation in coding competitions indicate a proactive, collaborative, and driven individual. The focus on building 'real-world GenAI products at scale' aligns well with a product-oriented AI team.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership and collaboration skills through their involvement in student associations and internships. Their project descriptions indicate an ability to work in teams and deliver client-facing solutions. The focus on privacy-first design and real-world problem-solving suggests a practical and responsible approach to engineering. However, as a fresher, direct experience in complex operational environments or handling production incidents is likely limited.