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Enter

ROHIT
YADAV

AI/ML Engineer & Data Analyst focused on building practical, scalable, and data-driven solutions. I combine machine learning, predictive analytics, data engineering, and applied AI to turn complex data into intelligent systems and actionable insights. Hands-on experience with recommendation systems, vector retrieval, graph-based ML, scalable data pipelines, and MLOps, backed by a strong foundation in cybersecurity and VAPT.

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01
02 · Profile — The Throughline

THE
THROUGHLINE

AI/ML Engineer & Data Analyst with hands-on experience building data-driven solutions across predictive modeling, recommendation systems, and cybersecurity analytics. Experienced in developing end-to-end machine learning workflows, customer churn prediction models, two-tower recommendation systems with vector retrieval, and graph-based threat detection using real-world datasets. Strong foundation in data analysis, feature engineering, model evaluation, data quality, and identifying challenges such as class imbalance and dataset bottlenecks. Combines applied machine learning and analytical skills with practical cybersecurity experience in VAPT and ethical hacking to build reliable, data-driven, and security-conscious solutions.

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01 · Propulsion — Data Architecture & Applied ML

ROHIT'S
ARSENAL

Languages & Scripting

Python, SQL, Bash, JavaScript, TypeScript

Machine Learning & AI

PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, FAISS, RAG, NLP, Computer Vision, Recommendation Systems, Graph Neural Networks, MLOps

Data Analytics & Pipelines

Pandas, NumPy, Power BI, Exploratory Data Analysis (EDA), Statistical Analysis, Feature Engineering, Data Validation, Schema Drift Detection, Data Pipelines, Pipeline Optimization

Databases & Data Stores

PostgreSQL, MySQL, Microsoft SQL Server (T-SQL), SQLite

Backend & Infrastructure

FastAPI, REST APIs, Docker, Streamlit, Linux, Git, GitHub

Cybersecurity & VAPT

VAPT, OWASP Top 10, Kali Linux, Burp Suite, Nmap, Wireshark, Metasploit, OWASP ZAP

03
03 · Projects — Engineering Case Studies

FEATURED
PROJECTS

Three engineering case studies spanning <5ms vector retrieval, GraphSAGE threat detection, and predictive churn modeling.

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Case File — 01

TWO-TOWER
FAISS ENGINE

RECOMMENDER SYSTEMS & VECTOR SEARCH · PUBLIC REPO
TWO-TOWER FAISS ENGINE
🌐 Public RepoComplete & Published on GitHub
github.com/mr-radix/two-tower-faiss-recommender ↗

KEY METRICS

  • • <5ms Vector Retrieval Latency across Top-200 Candidates
  • • 25M Interactions Processed (MovieLens 25M Dataset)
  • • 0.186 NDCG@10 & 0.168 Recall@10 Benchmark

SYSTEM SUMMARY

Solved candidate retrieval and ranking scaling bottlenecks for large-scale recommendation systems with 162K users and 62K items. Engineered a dual-encoder PyTorch Two-Tower embedding model generating 64-dim dense vectors coupled with GPU-accelerated FAISS index search. Implemented an XGBoost re-ranking pipeline with TF-IDF cold-start fallback and deployed an interactive Streamlit analytics app.

TECH STACK

Python · PyTorch · FAISS · XGBoost · Scikit-learn · Pandas · NumPy · Streamlit
04.1
Case File — 02

TELCO CHURN
ENGINE

PREDICTIVE ANALYTICS & RISK SCORING · PUBLIC REPO
TELCO CHURN ENGINE
🌐 Public RepoComplete & Published on GitHub
github.com/mr-radix/telco-customer-churn-prediction ↗

KEY METRICS

  • • 81.4% Recall & 0.813 ROC-AUC Accuracy
  • • 7,032 Customer Accounts Processed across 21 Features
  • • 5-Fold Stratified Cross-Validation with Zero Data Leakage

SYSTEM SUMMARY

Built an end-to-end customer churn prediction engine to proactively identify at-risk enterprise accounts and prevent revenue loss. Designed zero-leakage preprocessing pipelines and trained a Gradient Boosting classifier optimized via 5-fold stratified GridSearchCV. Deployed a real-time risk-scoring dashboard allowing operations teams to inspect churn probabilities and key risk drivers.

TECH STACK

Python · Scikit-learn · Gradient Boosting · Pandas · NumPy · Matplotlib · Streamlit
04.2
Case File — 03

CHAINGUARD
GRAPH AI

GRAPH AI & BLOCKCHAIN SECURITY · DEPLOYED APPLICATION
CHAINGUARD GRAPH AI
🌐 Live ApplicationPublic Repo Complete & Published on GitHub

KEY METRICS

  • • ~2.9M BitcoinHeist Records & Elliptic Graph Analysis
  • • XGBoost + GraphSAGE + Isolation Forest Architecture
  • • Multi-Hop Fund-Flow & Suspicious Activity Analysis

SYSTEM SUMMARY

Built and deployed a multi-chain cryptocurrency threat detection and forensic tracing platform combining Graph Neural Networks, XGBoost, and anomaly detection. Uses GraphSAGE for temporal illicit-node classification, XGBoost for ransomware classification, and Isolation Forest for behavioral anomaly detection. Utilizes BFS/DFS graph traversal, Union-Find UTXO clustering, peel-chain and fan-out/fan-in analysis to trace suspicious fund flows and generate explainable forensic risk assessments.

TECH STACK

Python · PyTorch · PyTorch Geometric (PyG) · NetworkX · XGBoost · Scikit-learn · Pandas · NumPy · FastAPI · REST API · Docker · Render
04.3
04 · Engineering Lab — Live Interactive Technical Workbench

RADIX UNIFIED
TECHNICAL LAB

Interactive multi-tool engineering laboratory spanning Transact-SQL, VAPT security defense, Azure ML inference, OpenTelemetry distributed tracing, hacker CLI terminal, and Power BI DAX executive analytics.

LIVE INTERACTIVE ENGINEERING WORKBENCH

RADIX UNIFIED TECHNICAL LAB

Test real, working engineering logic directly in your browser. Run queries in the T-SQL Studio, model data in the Power BI DAX Studio, scan payloads in the VAPT Security Inspector, calibrate thresholds in the Tabular ML Studio, inspect tensor neural pipelines in the Deep Learning Lab, analyze spans in the OpenTelemetry Tracer, or run commands in the live Radix CLI Terminal.

DATABASE: MS_SQL_SERVER_2022 // DATASET: ENTERPRISE_TRANSACTIONS✦ Verified Skill: DP-080 Transact-SQL ↗
LOAD T-SQL PRESETS:
query_console.sql
8 RECORDS MATCHED  · EXECUTION TIME: 14.2ms
IDCUSTOMERCATEGORYAMOUNTREGIONSTATUSRISKSCORETIMESTAMP
108Hyperion AIAI Inference Compute$22,000APACFlagged0.942026-10-06
111Pinnacle SystemsCloud Infrastructure$16,800US-EastApproved0.192026-10-04
107CyberDyne IncSecurity Gateway$15,400US-EastApproved0.052026-10-06
112Solaris CloudAI Inference Compute$14,200APACFlagged0.882026-10-04
102Nexus LtdAI Inference Compute$12,400EU-WestApproved0.082026-10-09
105Quantum SoftAI Inference Compute$9,800US-EastApproved0.222026-10-07
110Omega LabsSecurity Gateway$8,700US-WestApproved0.112026-10-05
104Zenith TechDatabase Storage$6,200US-WestApproved0.152026-10-08
WANT TO RUN THIS ARCHITECTURE IN YOUR PRODUCTION STACK?All code, DAX models, security guardrails, MLOps automation, and T-SQL optimizations shown here are 100% authored by Rohit Yadav.
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Intelligence & Cybersecurity

CYBERSECURITY &
THREAT INTEL

Hands-on Vulnerability Assessment & Penetration Testing (VAPT) across web applications & networks, paired with AI-driven threat intelligence systems.

WORK EXPERIENCE · ASD CYBER SECURITY AND CONSULTANT
CYBER SECURITY ANALYST INTERNJan 2025 – Mar 2025 (Remote)
📜 Verified CertificateASD Cyber Security Internship
👁 View Certificate ↗

KEY AUDITS & RESPONSIBILITIES

  • VAPT Audits: Conducted systematic Vulnerability Assessment and Penetration Testing (VAPT) across authorized client web applications and enterprise networks.
  • Vulnerability Remediation: Identified, cataloged, and prioritized over 10 high-severity security vulnerabilities mapped to the OWASP Top 10 framework.
  • Security Tooling & Automation: Automated network discovery, reconnaissance, and simulated exploitation workflows using Kali Linux, Burp Suite, Nmap, Wireshark, and OWASP ZAP.
  • Technical Reporting: Authored comprehensive audit-ready remediation advisories and collaborated with engineering leads to patch critical exploitation paths.

TECH STACK & TOOLING

Kali Linux · Burp Suite · Nmap · Wireshark · Metasploit · OWASP Top 10 · OWASP ZAP · Vulnerability Assessment
04.A
Curriculum Vitae & Resume

THE
RECORD

Education, professional certifications, and technical qualifications. Preview or directly download specialized resumes below for AI/ML Engineering or Data Analytics roles.

AI / ML Engineer

AI / ML Engineer Resume

Vector Retrieval (FAISS) · PyTorch & Transformers · Recommender Systems · Graph AI

👁 Preview Resume ↗⬇ Direct Download (PDF)
Data Analyst

Data Analyst Resume

Exploratory Data Analysis · Schema Drift & Pipelines · SQL · Power BI Dashboarding

👁 Preview Resume ↗⬇ Direct Download (PDF)
2024 – 2027
In Progress

Bachelor of Science (B.Sc)

  • Ch. Badan Singh P.G. College (Raja Mahendra Pratap Singh State University), Etah, UP
Nov 2025 –
Present

Professional Certification Program in AI & Data Science

  • Intellipaat & DRISHTI CPS, IIT Indore — Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI & LLMs
Oct 2026
Certification

Operationalize Machine Learning & Generative AI Solutions (AI-300)

  • Microsoft Learn Credential: End-to-end curriculum in production MLOps, Azure Machine Learning lifecycle, and Generative AI application engineering.
  • Competencies Verified: GenAIOps solution design, AI agent experiment optimization, model tracing & monitoring, automated CI/CD evaluation pipelines, and hyperparameter tuning with MLflow.
◈ Curriculum Roadmap & 13 Verified Module Badges (GenAIOps & Azure ML)
Phase 01Azure Machine Learning Foundations & Experimentation
Azure ML workspace architecture, automated data assets, model training scripts, and experiment logging with MLflow.
✦ Machine learning foundations in Azure ↗Azure ML ArchitectureAzure ML workspace architecture, compute targets, and data storage foundations for scalable cloud ML workflows.Verified Achievement ↗✦ Experiment with Azure Machine Learning ↗Experimentation & SDKConfigure training runs, manage environments, and track experiments using the Azure ML Python SDK.Verified Achievement ↗✦ Track training scripts & models with MLflow ↗MLflow IntegrationAutolog metrics, parameters, and artifacts with MLflow integration inside Azure Machine Learning.Verified Achievement ↗
Phase 02MLOps Automation & Production Deployment
Hyperparameter search spaces, automated CI/CD pipeline triggers with GitHub Actions, and production model serving.
✦ Hyperparameter tuning with Azure ML ↗Hyperparameter TuningAutomate hyperparameter optimization sweeps with early termination policies and Bayesian sampling.Verified Achievement ↗✦ Run pipelines in Azure Machine Learning ↗Pipeline OrchestrationBuild, orchestrate, and automate reproducible multi-step machine learning workflows and data pipelines.Verified Achievement ↗✦ Automate model training with GitHub Actions ↗CI/CD MLOps AutomationImplement CI/CD automation to trigger Azure ML pipeline execution upon GitHub pull requests and merges.Verified Achievement ↗✦ Deploy & monitor models in Azure ML ↗Model Serving & TelemetryDeploy models to managed online endpoints with auto-scaling, blue/green rollouts, and data drift telemetry.Verified Achievement ↗
Phase 03GenAIOps & AI Agent Orchestration
Generative AI solution architecture, prompt lifecycle management in Microsoft Foundry, and structured agent experiments.
✦ Plan & prepare a GenAIOps solution ↗GenAIOps ArchitectureArchitect end-to-end operational workflows for LLMs, prompt engineering lifecycle, and enterprise AI safety.Verified Achievement ↗✦ Manage prompts for agents in Microsoft Foundry ↗Prompt Engineering & GitVersion-control prompt templates, integrate system instructions, and manage LLM assets with GitHub sync.Verified Achievement ↗✦ Evaluate & optimize AI agents via experiments ↗Agent ExperimentationRun structured comparative benchmarking, latency/cost profiling, and response quality scoring for AI agents.Verified Achievement ↗
Phase 04Evaluation, Tracing & Production Observability
Continuous automated evaluation pipelines, OpenTelemetry/tracing instrumentation, and runtime GenAI application monitoring.
✦ Automated AI evaluations (Foundry + GitHub Actions) ↗Automated AI Eval GatesIntegrate automated quality, safety, and groundedness evaluations directly into CI/CD build gates.Verified Achievement ↗✦ Monitor generative AI applications ↗GenAI Runtime MonitoringTrack real-time token consumption, latency spikes, jailbreak attempts, and user sentiment in production.Verified Achievement ↗✦ Analyze & debug GenAI apps with tracing ↗Distributed Tracing & DebuggingInstrument GenAI applications with OpenTelemetry tracing to inspect multi-step agent tool calls and RAG pipelines.Verified Achievement ↗
Oct 2026
Certification

DP-080: Querying Data with Microsoft Transact-SQL

  • Microsoft Learn Credential: Comprehensive relational database querying, analytical SQL operations, and database manipulation in Microsoft SQL Server / T-SQL.
  • Competencies Verified: Complex relational JOINs, multi-level subqueries, GROUP BY aggregations, scalar & window built-in functions, data sorting/filtering, and data modification (DML).
◈ Curriculum Roadmap & 6 Verified Module Badges (Transact-SQL Mastery)
Phase 01T-SQL Syntax & Data Retrieval Fundamentals
Relational SELECT querying fundamentals, column aliasing, predicate filtering with WHERE, and sorting with ORDER BY.
✦ Introduction to Transact-SQL ↗T-SQL Syntax & RetrievalCore SQL syntax, relational query structure, column aliasing, and data types in Microsoft SQL Server.Verified Achievement ↗✦ Sort & filter results in T-SQL ↗Filtering & SortingAdvanced row filtering using WHERE, boolean logic, LIKE patterns, and deterministic sorting via ORDER BY.Verified Achievement ↗
Phase 02Relational Joins & Complex Subquery Construction
Multi-table relational schema navigation via INNER/OUTER/CROSS JOINs and correlated & scalar subqueries.
✦ Combine multiple tables with JOINs in T-SQL ↗Multi-Table JoinsRelational data integration using INNER, LEFT, RIGHT, FULL OUTER, and CROSS JOIN operations.Verified Achievement ↗✦ Write Subqueries in T-SQL ↗Subqueries & PredicatesScalar, multi-valued, and correlated subqueries, along with EXISTS and IN predicate logic.Verified Achievement ↗
Phase 03Aggregations, Functions & Data Modification
Analytical GROUP BY aggregations, HAVING clauses, scalar/window functions, and DML write operations (INSERT, UPDATE, DELETE).
✦ Built-in functions & GROUP BY in T-SQL ↗Aggregations & FunctionsAggregation with SUM/COUNT/AVG, categorical grouping via GROUP BY & HAVING, and scalar functions.Verified Achievement ↗✦ Modify data with T-SQL ↗DML OperationsData manipulation language (DML) statements: INSERT, UPDATE, and DELETE operations with transaction safety.Verified Achievement ↗
Certification

Certified Ethical Hacker (CEH) & VAPT Training

  • ASD Cyber Security and Consultant — Ethical Hacking, VAPT, Vulnerability Assessment, Threat Intelligence, OWASP Top 10
07
Signal

SEND A
SIGNAL

I'm currently open to Data Analyst, AI/ML Engineering, and Cybersecurity consulting roles & opportunities. Reach out to collaborate.

Email · rohityadav75059@gmail.comGitHub · mr-radixLinkedIn · rohit-yadavPhone · +91 7505960910
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