Available for AI & Systems Engineering Roles

Ananay Chaudhry

AI Infrastructure Engineer•MLOps•LLM Systems•Security

Building robust machine learning infrastructure, high-throughput pipeline architectures, and secure AI evaluation guardrails. Focused on reliability and defensive design.

Resume.pdf GitHub LinkedIn

ananay@ZelWolf:~

bash

ananay@ZelWolf:~#

AI Engineering specialist designing clean backend telemetry, operational MLOps pipelines, and secure model integrations.

ananay@cybertron:~#

{
  "focus_vectors": ["LLM Guardrails", "PII Redaction Engines", "Data Ingestion Automation"],
  "design_motto": "Build defensively. Isolate failures. Log metrics.",
  "availability": "Graduating 2026 // Targeting Global Engineering Roles"
}

> Engineering Repositories

Financial Insights Copilot

An AI-Powered 10-K Analyzer Microservice

RAG Architecture

Built an end-to-end intelligent extraction engine designed to digest unstructured SEC 10-K filings. Leveraged LangChain layers linked with a custom FAISS context retrieval pipeline to answer natural-language commands backed up by explicit source-citations and z-score anomaly filters.

SEC Filing→Parser→Embeddings→FAISS DB→LangChain→Citation Layer

FAISSLangChainFastAPIDocker

Automated Demand Forecasting Pipeline

Continuous Integration & Lineage Production MLOps Engine

Production MLOps

Architected an end-to-end self-correcting MLOps pipeline automating retraining and model registration triggers via GitHub Actions. Tracks full experimental lineage using DVC and MLflow, serving production-grade containerized XGBoost microservices monitored continuously by Evidently AI for data and concept drift telemetry.

Data Ingestion→DVC Lineage→GitHub Actions→MLflow Tracking→Docker API→EvidentlyAI Drift

MLflowDVCEvidentlyAIGitHub Actions

DataShield

Automated Sensitive Data Redaction Pipeline

Security

Designed an automated high-throughput privacy processing framework to intercept unstructured context payloads, programmatically stripping/masking PII configurations to safeguard backend training contexts against implicit data leakage vectors.

Context Payload→Regex Pipeline→Redaction Engine→Masking Layer→Secure Payload

PythonRegexPipelineSecurityAutomation

> Industry Deployments

Pratinik Infotech

ML Engineer (Summer Intern) — Remote

June 2025 — Aug 2025

▪Developed an end-to-end NLP routing engine to dynamically cluster 78,312 unlabeled enterprise system tickets into 5 functional core routing metrics.
▪Engineered noise reduction logic extracting distinct POS structural tokens using NMF and TF-IDF matrix variations, manufacturing high-quality labeled sets from scratch.
▪Optimized classification targets via XGBoost, securing a 0.91 F1-score with execution times scaling cleanly down to ~6.5ms inference latency.

XGBoostNLP PipelinesNMF TokenizationFeature Engineering

> Verified Certifications

Gemini Enterprise Agent DevelopmentCertified Partner Specialist

Issued by Google Cloud

Microsoft Certified: Azure AI Apps and Agents Developer AssociateAI-103

Issued by Microsoft

Microsoft Certified: Azure AI Cloud Developer AssociateAI-200

Issued by Microsoft

Certified LLM Security ProfessionalRed Team Leaders

Issued by Red Team Leaders

Deep RL CertificationHugging Face

Issued by Hugging Face

Apache Airflow CertificationAstronomer

Issued by Astronomer

> Technical Skills

Languages

Python, SQL, Bash

Machine Learning

PyTorch, TensorFlow, Scikit-Learn, Pandas, NumPy

Frameworks & Libraries

LangChain, LlamaIndex, FastAPI, FAISS, ChromaDB

Cloud & MLOps

AWS (SageMaker, Bedrock), Azure ML Studio, Docker, Kubernetes, MLflow, DVC, Airflow, GitHub Actions

Let's Build Something.

Currently open for AI Engineering, MLOps, and Infrastructure roles. If you're looking for someone to scale your backend or secure your LLMs, my inbox is open.

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