Mohamed Fasidh
Generative AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Generative AI Engineer with less than a year in Agentic AI & RAG Pipelines.
Production-focused Generative AI Engineer with end-to-end experience designing and shipping Agentic AI systems, RAG pipelines, and LLM-powered applications using Python, LangChain, LangGraph, FastAPI, and leading vector databases. Delivered measurable production impact — 30% improvement in AI-driven matching accuracy and 50% + reduction in manual processing effort — across 100K-1M record workloads. Hands-on with Prompt Engineering, vector database-backed semantic search, MCP Server integration, multi-agent orchestration, Docker, and Cloud fundamentals (AWS). A fast-learning fresher-level candidate with real production deployments and a passion for building AI applications that reason and execute at scale.
Kamaraj College of Engineering and Technology
B.Tech · Computer Science & Engineering (AI & Data Science)
August 1, 2021 – June 30, 2025
Quantanics TechServ Private Limited
Generative AI Engineer - Enterprise AI Platform
May 1, 2025 – October 1, 2025
India
Medical RAG Chatbot — Production LLM Application LIVE
June 24, 2026 – Present
Built and deployed a production RAG chatbot combining LangChain orchestration, Pinecone vector database, and GPT-4/Gemini LLM APIs – live on Railway with real user traffic, demonstrating full AI application development lifecycle from prompt engineering through cloud deployment. Engineered the complete RAG pipeline end-to-end: document ingestion → chunking → embedding generation → Pinecone vector indexing → semantic retrieval → LLM-augmented response generation, achieving accurate context-grounded answers across complex medical knowledge domains. Designed production-grade FastAPI backend with compliance-aware logging, auditability, and concurrent load optimisation – demonstrating ability to build and deploy GenAI applications that are robust, observable, and production-ready.
JobScreenAI - Agentic AI Recruitment Intelligence Platform
June 24, 2026 – Present
Architected a multi-agent Agentic AI platform with LangGraph-orchestrated workflows – agents autonomously retrieve, score, rank, and evaluate candidates using RAG retrieval + ML scoring pipelines across 500-1,000 records per run with zero manual intervention. Implemented vector database-backed semantic search combining embedding strategies and structured ML models – reduced manual screening effort by 50%+ and elevated decision quality for business stakeholders at scale. Delivered a multi-tiered FastAPI REST API with data quality checks, compliance-aware logging, and auditability – production-grade Agentic AI system built from the ground up, containerised with Docker and deployed on AWS.
Math Tutor LLM — QLORA Fine-Tuning Pipeline (Gemma-2B)
June 24, 2026 – Present
Fine-tuned Gemma-2B using QLORA (LoRA r=16, alpha=32, NF4 4-bit via BitsAndBytes) on a custom 117-example instruction dataset – domain-adapted LLM demonstrating hands-on expertise in parameter-efficient fine-tuning for real-world AI applications. Implemented full fine-tuning pipeline: dataset curation → tokenisation → LoRA adapter training → quantized inference – end-to-end AI/ML fundamentals demonstrated in practice.
Real-Time Collaborative Whiteboard – Distributed Event-Driven Platform
June 24, 2026 – Present
Architected a fault-tolerant real-time collaboration system supporting 200-500 concurrent users at sub-100ms latency using event-driven architecture and distributed state consistency – backend reliability and scalability applicable to enterprise GenAI infrastructure. Built Kafka + Redis messaging layer sustaining 50+ simultaneous sessions with resilient delivery and zero data loss – system design skills directly transferable to scalable AI workflow orchestration.
Google Cloud Generative AI Learning Path
Google Cloud Skills Boost
June 1, 2026 – Present
Engineering Data Structures & Algorithms
Coursera
June 1, 2026 – Present
Python for Data Science
Coursera
June 1, 2026 – Present
Cloud OCI Foundations Associate
Oracle
June 1, 2026 – Present
Generative AI
Google Cloud Skills Boost
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a Generative AI Engineer role. Their projects showcase a proactive approach to building complex AI systems from scratch, including full-stack development and deployment. The diversity of projects, from medical chatbots to recruitment platforms and real-time collaboration systems, indicates adaptability and a broad interest in applying AI across different domains. The mention of contributing to open-source AI tooling further highlights a collaborative and community-oriented mindset. The candidate's education in Computer Science & Engineering (AI & Data Science) and numerous certifications in Generative AI and Cloud reinforce their commitment to the field.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong focus on production readiness, observability, and performance optimization, indicating a practical and results-oriented approach. The emphasis on compliance-aware logging, auditability, and security (RBAC, JWT/OAuth) suggests a mature understanding of enterprise requirements. The ability to work with multi-agent systems implies strong problem-solving and architectural thinking.