Inzemam Baig

Senior Backend Engineer | AI & LLM Applications | Cloud Native

I Design microservices and AI-driven systems that scale

About

I'm a software developer with a passion for building products that live on the internet. I care deeply about crafting experiences that are fast, accessible, and a joy to use. I thrive at the intersection of design and engineering — where aesthetics meets clean, scalable code.

Over the years I've worked across a wide range of environments — from early-stage startups to established tech companies — shipping everything from consumer-facing apps to internal developer tools. I enjoy the full journey from idea to deployed product.

When I'm not at my desk, you can find me listening to podcasts, watching stand-up comedy, cooking my favorite dishes, or working out.

Experience

  1. Jan 2024 — Present

    Senior Software Developer · Infosys

    Architected and deployed microservices on AWS using Java, Spring Boot, and PostgreSQL for high-volume banking applications. Led monolith-to-microservices migration, improving system scalability and deployment frequency by 30%. Containerized applications using Docker and orchestrated with Kubernetes on AWS, ensuring high availability. Implemented infrastructure as code using Terraform, reducing provisioning time from days to hours. Optimized JVM performance and database queries, reducing response latency by 20%. Established API security standards, encryption protocols, and compliance controls.

    • Java
    • Spring Boot
    • Microservices
    • PostgreSQL
    • Docker
    • Kubernetes
    • AWS
    • Terraform
  2. Oct 2021 — Dec 2023

    Senior Java Developer · Cognizant

    Led secure cloud data migration of sensitive healthcare data from on-premise systems to Google Cloud Platform (GCP) for a US-based client, with primary focus on HIPAA compliance and protection of Patient Health Information (PHI). Collaborated with compliance teams to validate adherence to healthcare regulations, ensuring auditability and risk mitigation. Coordinated with US-based technical teams to validate data post-migration for downstream analytics. Designed and implemented event-driven architecture using Apache Kafka for real-time data processing. Built cloud-native microservices ensuring seamless inter-service communication and scalability. Developed asynchronous task scheduling systems for intelligent data flow automation.

    • Java
    • Spring Boot
    • GCP
    • Apache Kafka
    • Microservices
    • PostgreSQL
    • Docker
  3. Feb 2019 — Sept 2021

    Java Developer · Tata Consultancy Services

    Developed backend modules using Spring and PostgreSQL to automate tax return processing. Improved performance by 20% via optimized multithreading and JMeter-based load testing. Integrated Prometheus for performance monitoring and bottleneck detection.

    • Java
    • Spring
    • PostgreSQL
    • JMeter
    • Prometheus
  4. Jul 2018 — Jan 2019

    Python Developer · Lets Nurture

    Built a Python Flask-based geolocation service converting coordinates into human-readable location codes.

    • Python
    • Flask

Projects

  1. GenAI Customer Onboarding & Personalization Platform

    POC for Danske Bank — a GenAI-powered onboarding assistant using AWS Bedrock Agents. Built LLM-driven workflows for customer data collection, KYC validation, and product recommendations. Integrated AWS Knowledge Bases for context-aware responses and designed an end-to-end onboarding-to-fulfillment flow using microservices.

    • AWS Bedrock
    • LLM
    • Microservices
    • AWS Knowledge Bases
    • GenAI
  2. AI Mock Interview & Resume Analysis Platform

    AI-powered platform to analyze resumes and generate role-specific interview questions. Integrated AI models to extract skills and experience from resumes, implemented LLM-based answer evaluation with structured feedback reports, and designed a complete workflow from resume upload to interview simulation.

    • AI/LLM
    • Resume Parsing
    • Next.js
    • TypeScript
  3. RAG-Based PDF Q&A System

    Developed a full-stack RAG-based PDF Q&A system using FastAPI, LangChain, FAISS, and OpenAI, with a visual pipeline interface to demonstrate chunking, embeddings, semantic search, and LLM response generation.

    • FastAPI
    • LangChain
    • FAISS
    • OpenAI
    • RAG

Contact

I'm currently open to new opportunities. Whether you have a question, a project idea, or just want to say hi — my inbox is always open. I'll do my best to get back to you!