Headline

Software Engineer II @ Wayfair | Supply Chain and Retail Technology | TIET’24 | AI Engineering | Backend & Distributed Systems | Java, Spring Boot, Kafka, Kubernetes

About

Hi there, I'm a Software Engineer at Wayfair with experience building scalable backend systems, distributed architectures, and production AI applications. At Wayfair, I design and develop backend microservices using Java, Spring Boot, Kafka, PostgreSQL, Redis, Docker, and Kubernetes. My work spans API design, event-driven systems, distributed workflows, data migrations, monolith decomposition, and large-scale platform modernization while maintaining reliability in production environments. I have built production AI solutions leveraging LLMs, LangGraph, MCP, RAG, vector databases, multi-agent systems, and workflow automation. My recent work includes developing AI-powered operational assistants, automated investigation workflows, and self-healing systems that integrate enterprise tools, datasets, and monitoring platforms to improve operational efficiency. Prior to this, I worked on federated learning, differential privacy, and privacy-preserving distributed systems, strengthening my foundations in distributed computing, scalability, data correctness, and system design. Core areas of interest include Distributed Systems, Backend Engineering, AI Infrastructure, Agentic Systems, System Design, and Applied Machine Learning. Strong in Data Structures & Algorithms (C++) and building reliable, scalable systems from design to production.

Location

India

AI stack

anthropic apis

claude code

claude

cursor

elevenlabs

gemini

Work experience

Software Engineer II at Wayfair, Sep 2026 - Present · 1 mo, Bengaluru, Karnataka, India

Software Engineer I at Wayfair, Dec 2024 - Present · 1 yr 10 mos, Bengaluru, Karnataka, India, AI Platform & Workflow Automation (Synapse): • Built a production AI assistant using LangGraph, MCP, BigQuery, n8n, LLMs, RAG, and vector databases to automate facility investigations and operational workflows, reducing manual effort by ~30%. • Developed multi-agent workflows with ReAct reasoning, tool-calling, and workflow orchestration across supplier and warehouse lifecycle systems. • Built RAG pipelines over BigQuery metadata using vector search and MCP-integrated tools for intelligent dataset discovery and workflow automation. • Developed event-driven self-healing workflows triggered by Datadog alerts, automating log analysis, root-cause investigation, remediation, and escalation. • Implemented observability and tracing for AI agents, monitoring latency, token usage, costs, tool outcomes, and workflow reliability. Facility Lifecycle & Platform Migration • Led migration of 5 microservice clients from Facility Core (FC) to Facility DataHub (FDH), supporting backend ownership transition across distributed systems. • Built Kafka consumers in Java/Spring Boot to process warehouse lifecycle events and maintain event-driven consistency during platform decommissioning. • Developed automated reconciliation pipelines to validate cross-system data consistency and migration accuracy. • Supported PostgreSQL schema evolution, data migrations, REST APIs, and backend services for facility, supplier, and warehouse workflows. Legacy Modernization & Service-Oriented Architecture • Migrated 7 legacy clients (74 DAO files) from direct SQL access to FDH APIs, reducing database coupling and enabling microservices adoption. • Implemented hybrid read architectures combining API and monolith data sources to support gradual ownership separation. • Delivered backward-compatible APIs and zero-downtime production rollouts while maintaining reliability and scalability.

Software Engineer Intern at Wayfair, Sep 2024 - Dec 2024 · 4 mos, Bengaluru, Karnataka, India

Research Intern at Thapar Institute of Engineering & Technology, Feb 2024 - Jun 2024 · 5 mos, Patiala, Punjab, India, Developed an advanced federated learning framework for the detection of Lung Disease(s) from chest X-ray images, leveraging deep learning models like ResNet-50 and Inception V3. By integrating soft computing techniques, including fuzzy logic and random forest classifiers, the project significantly enhanced the model's robustness and handling of uncertainties. Implemented differential privacy and secure multi-party computation to ensure data confidentiality, complying with stringent healthcare regulations. Achieved high diagnostic accuracy ~96% while maintaining data privacy, showcasing the potential of AI technologies in medical imaging and diagnostics.

SDE Intern at Veritos Infosolutions Pvt. Ltd., Jun 2023 - Jul 2023 · 2 mos, Remote

Education

Bachelor of Engineering - BE, Computer Engineering, Thapar Institute of Engineering & Technology

Public links

github: https://github.com/AdiK1806

leetcode: https://leetcode.com/adik__18