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Contact Information

Name Md Ayan Arshad
Professional Title AI Engineer
Email ayanarshad2002@gmail.com
Phone +91 7377869686
Location Chennai, India
GitHub https://github.com/AyanArshad02

Professional Summary

GenAI Engineer with ~2 years of production experience building reliable backend systems for RAG and agentic workflows. Architected a multi-tenant RAG chatbot at Softeon serving 500+ enterprise tenants with hybrid retrieval, tenant isolation, and observability. Built FastAPI-based RAG systems with Claude MCP integrations for agentic tool use. Passionate about context engineering, agent workflows, and shipping high-quality production AI systems. Available for US EST / EU hours.

Experience

  • 2025 -

    Chennai, India

    GenAI Engineer
    Softeon
    • Multi-Tenant RAG Chatbot — 500+ Enterprise Tenants: Architected and led delivery of a production multi-tenant WMS chatbot; reported directly to AVP of Data Science.
    • Designed HLD and LLD for multi-tenant architecture using AWS (Cognito, S3, DynamoDB, Lambda, EC2); defined data flows, access boundaries, and deployment strategy.
    • Solved tenant isolation at the vector DB layer (Pinecone namespaces), eliminating cross-tenant data-leak risk across 500+ enterprise tenants.
    • Built hybrid retrieval with reranking and grounding validation; pushed faithfulness from 0.67 → 0.91 and unsupported claim rate below 4%.
    • Led a 2-engineer team: task breakdown, sprint planning, code reviews, and on-time delivery.
    • AI-Driven Ticket Resolution System: Led end-to-end design and delivery for Softeon’s WMS support division, owning the project from architecture (HLD/LLD) to production rollout.
    • Built a statistically evaluated semantic retrieval system over historical tickets and documentation using PostgreSQL and ChromaDB, significantly reducing manual lookup time and unresolved ticket backlog.
    • Performed EDA on historical support data to analyse ticket distributions, resolution patterns, and retrieval effectiveness; optimised relevance thresholds and retrieval depth.
    • Designed evaluation workflows to assess retrieval precision, response usefulness, and failure cases, enabling continuous system improvement.
    • Implemented monitoring and logging to support reliability and future extensibility.
  • 2024 - 2025

    Remote

    GenAI Engineer
    Second Brain Labs (SBL)
    • Built a LinkedIn outreach chatbot integrated with the LinkedIn API: personalised messages, multi-turn conversation handling, and lead qualification across multiple client accounts simultaneously.
    • System ran on GPT-4 with real campaign traffic.

Awards

  • 2024
    Topped Programming in Python & System Command
    IIT Madras

    Topped the Programming in Python & System Command course.

  • 2024
    Machine Learning Practice Coding Exam
    IIT Madras

    Scored 85+ in the Machine Learning Practice Coding Exam.

  • 2024
    ML Project — System Threat Forecaster
    IIT Madras

    Scored 93/100 in the ML Project final evaluation.

Skills

GenAI & RAG: LangChain, LangGraph, LangSmith, Custom Agent Frameworks, RAGAS, OpenAI API, Claude, AWS Bedrock, Hybrid Retrieval, Grounding Validation, Cohere
LLMOps & Cloud: AWS (EC2, Lambda, S3, DynamoDB, Cognito, ECR, CloudWatch), Docker, Kubernetes, MLflow, DVC, ZenML, CI/CD
ML & Modelling: XGBoost, LightGBM, scikit-learn, Statistical Modelling, Feature Engineering, EDA, Bias-Variance Analysis
Systems Design: HLD/LLD Architecture, Multi-Tenant Systems, API Design, Microservice Interactions, Observability
Programming: Python, SQL, FastAPI, Bash
Databases: PostgreSQL, Qdrant, ChromaDB, Pinecone, DynamoDB, MongoDB, Redis

Languages

English : Professional working proficiency

Interests

Writing & Open Work: Technical blog on Dev.to: deep dives on RAG, Agentic AI, multi-tenancy, retrieval failures, and production system design., Production GenAI engineering content on LinkedIn (2,300+ followers) — decisions and tradeoffs tutorials never cover., 1 free RAG architecture review per month, 48-hour turnaround.

Projects

  • kapa.ai (YC S23) Inspired Multi-Tenant RAG System

    Production-grade RAG backend using FastAPI. Ran 12+ retrieval pipeline combinations before settling on Heading-Aware chunking + OpenAI embeddings + Cohere reranker + Qdrant. RAGAS scores: 0.91 Faithfulness, 0.95 Context Recall, 0.89 Context Precision.

    • Implemented MCP server for seamless Claude Desktop / Cursor integration, enabling agentic tool use.
    • Clean 3-layer architecture (Orchestrator → Strategy → Implementation) with dependency inversion for easy LLM/vector-store swaps.
    • Enterprise features: multi-tenancy (isolated Qdrant collections), API key auth, Redis caching, PostgreSQL conversation memory, async ingestion, rate limiting, Prometheus metrics, LangSmith tracing.
    • Full Docker Compose stack (PostgreSQL, Qdrant, Redis) with comprehensive unit and integration tests.
  • Credit Card Fraud Detection — Full MLOps

    End-to-end fraud detection ML pipeline with class imbalance handling, SMOTE, and reproducible experiment tracking via MLflow.

    • Statistical EDA to analyse class imbalance, feature distributions, and leakage risks.
    • Evaluated models using ROC-AUC and precision-recall tradeoffs with MLflow experiment tracking.
    • Automated ML pipeline with CI/CD and retraining workflows using DVC and Kubernetes.
  • Vehicle Insurance Purchase Prediction

    ML pipeline to predict insurance purchase likelihood using tabular vehicle and customer data. Focused on probability calibration and threshold tuning to align with business risk.

    • Deployed using FastAPI with CI/CD and cloud infrastructure.
  • System Threat Forecaster (Malware Prediction)

    End-to-end ML pipeline to predict system infection using telemetry data. Built at IIT Madras; scored 93/100 in final evaluation.

    • EDA, feature engineering, and ensemble modelling using XGBoost and LightGBM.