Mohammed Zakir Nooraj
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
AI Engineer with 1+ years in LLM applications & GenAI systems
AI/ML-focused Data Scientist with experience building LLM-powered applications and data-driven systems end-to-end from data pipelines and RAG systems to production deployment. Proficient in Python, Node.js, Redis, and GenAl frameworks including LangChain and LangGraph. Experienced in implementing and integrating ServiceNow ITSM/HRSD modules with modern data and backend services to automate enterprise workflows. Passionate about applying Al, machine learning, and cloud technologies to solve high-impact business problems in fast-paced Agile environments.
GMR Institute of Technology
B.Tech · Information Technology
August 1, 2020 – June 30, 2024
IT4YOURBUSINESS
Jr Backend Engineer
December 1, 2024 – January 1, 2026
Hyderābād, Telangana, India
Andhra University
R&D Intern - Semantic Communication Systems
June 1, 2023 – October 1, 2023
Visakhapatnam, Andhra Pradesh, India
Enterprise RAG (Retrieval-Augmented Generation) Engine
June 1, 2026 – Present
Engineered an enterprise-grade Al assistant using LangChain and LLMs to synthesize accurate responses from heterogeneous unstructured sources (PDFs, web scrapes), significantly improving knowledge retrieval reliability. Optimized vector retrieval precision via advanced text chunking strategies and semantic indexing in ChromaDB, measurably reducing model hallucination rates. Deployed a scalable backend wrapper (FastAPI) exposing async model inference endpoints with sub-second latency for end-user interactions.
Team Tasker
June 1, 2026 – Present
Collaborative task and workflow management platform with real-time coordination, team productivity tracking, and scalable deployment architecture.
View ProjectNaukri Auto-Fill Assistant
June 1, 2026 – Present
AI-powered Chrome extension that automates repetitive Naukri application workflows, improving application speed and user productivity.
View ProjectTechMantra Enterprise Management Platform
June 1, 2026 – Present
Delivered a full-stack enterprise solution with Spring Boot microservices backend and a reactive React.js frontend, following strict MVC design patterns. Engineered optimized SQL queries and complex JPA relationships to manage large-scale datasets with ACID compliance and rapid data retrieval.
Multi-Agent AI System - Market Research QA Automation
June 1, 2026 – Present
Designed a stateful multi-agent AI system to automate QA validation of complex global survey logic (300+ page documents, 20+ languages), reducing validation time from 3-4 days to ~2 hours. Architected agent orchestration using LangGraph with cyclic test-verify-retry workflows for reliable and deterministic survey validation. Built a document parsing pipeline using pdfplumber, pandas, and Pydantic to convert survey logic trackers into a structured Golden Schema with strict LLM schema validation. Developed an autonomous semantic testing agent using Playwright + Python with an Extract-Reason-Act loop to navigate and test live survey interfaces. Implemented Agentic RAG to dynamically retrieve relevant logic snippets, overcoming LLM context limitations for large survey schemas. Optimized execution using a hybrid routing strategy (deterministic Playwright selectors + LLM reasoning), reducing inference cost and latency by ~40%. Built Auditor and Verifier agents using Levenshtein distance and embeddings to validate logs and detect hallucinated UI actions before execution. Deployed the system using Docker on Google Cloud Run, integrated LangSmith observability, and achieved 98% validation precision.
Holistic Brain MRI Segmentation, Classification & Survival Prognosis
June 1, 2026 – Present
Developed an end-to-end deep learning pipeline for brain tumor classification, segmentation, and survival prediction using MRI image data. Implemented a CNN-based multi-class classifier for meningioma, pituitary, glioma, and non-tumor types; designed a DeepLabV3 segmentation model for precise tumor region detection and area calculation. Built a 3D CNN for volumetric MRI feature extraction and survival regression, evaluated with MAE & RMSE metrics. Applied advanced augmentation, dropout regularization, and early stopping to address class imbalance and overfitting; evaluated using Accuracy, F1, Dice Coefficient, and IoU.
AWS Academy Graduate - Machine Learning Foundations
AWS Academy
June 1, 2026 – Present
Oracle Cloud Infrastructure 2023 Certified Foundations Associate
Oracle
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from enterprise RAG engines to multi-agent systems and medical imaging, indicates a broad interest in applying AI across various domains. The experience with both personal and academic projects, alongside an internship and a junior backend engineer role, shows a proactive learning approach and adaptability. The listed core skills and project technologies align well with a modern AI/ML and backend engineering culture.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong ability to work in fast-paced Agile environments, deliver end-to-end AI solutions, and engage in cross-functional collaboration. The detailed descriptions of problem-solving and optimization efforts suggest a proactive and results-oriented work attitude. The focus on technical documentation also indicates good communication and organizational skills.