WHO YOU’LL WORK WITH

This role typically reports to Sr Manager Software Engineering within Global Converse Technology (ITC).

Product Managers and Business Partners across Supply Chain, Planning, Finance, and Corporate functions
Software Engineers and Integration Engineers
Platform, DevOps, and Site Reliability Engineering (SRE) teams
Data Engineering, Analytics, Architecture, and Security teams
External technology vendors and system integrators

WHO WE ARE LOOKING FOR

We are seeking a Software Engineer II – Full Stack Agentic AI - for Global Converse within the India Technology Centre (ITC). You will build reliable, secure, and scalable systems— partnering across product, design, and platform teams to deliver meaningful consumer and enterprise experiences. This is a hybrid role that combines hands‑on software engineering with quality engineering and testing responsibilities.

You will contribute to building, testing, and delivering enterprise solutions that support Supply Chain, Planning, and Corporate Functions, Marketplace, Integration and Enterprise Data Analytics domains. The ideal candidate has strong coding fundamentals, understands end‑to‑end delivery, and brings a quality‑first mindset to everything they build.

Details on qualifications:

Bachelor’s degree in Computer Science, Engineering, or a related field.
Will accept any suitable combination of education, experience, and training.
3+ years of professional software engineering experience building and operating production systems.
Experience in microservices architecture, domain driven design, and RESTful API’s using languages such as Node.js and python
Experience using cloud services to build an integrated application (AWS - EC2, ECS, API gateway, Lambda, Relational and Non-relational Databases), and container orchestration (Docker/Kubernetes).
Experience with implementing CI/CD and leading teams with DevOps responsibilities of deploying and supporting the software in production
Proven delivery excellence in highly collaborative, multi disciplinary teams.
Expertise in microservices architecture, domain driven design, RESTful services,
Proficiency in one or more of: Java/Kotlin, JavaScript/TypeScript, Python, or Go.
Good to have experience with observability & monitoring tools (e.g., Splunk, SignalFx)
Familiarity with messaging and streaming platforms (e.g., Kafka, Event Hubs).
Knowledge of secure coding practices, identity and access management, and compliance considerations.
Experience testing and/or building integrations and data flows using APIs, messaging, and batch processing.
A flexible mindset, comfortable switching between engineering delivery
Exposure to AI assisted development or testing tools—or a strong interest in learning and applying AI to improve engineering productivity and quality
Experience with AI technologies or a strong interest and demonstrated eagerness to learn and apply AI driven solutions to improve software design, development, and operational efficiency.
Strong collaboration, communication, and growth mindset.
Strong understanding of Full Stack Proficiency: Strong skills in Python, TypeScript, or JavaScript, API integration, data connectors, SQL/No-SQL databases etc
AI/LLM Experience: Proven experience with Large Language Models (LLMs) and building AI agents.
Experience with RESTful APIs and database management (Dataverse or SQL).
Problem-Solving: Ability to debug agent behaviors, handle hallucinations, and optimize prompt engineering.
Knowledge of AWS/Azure OpenAI Service.

WHAT YOU’LL WORK ON

AI Agent Development/orchestration: Design, build, and implement AI agents and copilots using frameworks such as RAG
Explore and integrate AI-driven automation tools to enhance process efficiency and decision-making
Coordinate with cross-functional teams to ensure seamless integration and delivery.
Document processes, best practices, and maintain compliance with organizational standards.
Backend & Frontend Development: Create secure, high-quality, full-stack applications (React, Angular, or Vue.js for frontend?; Python/Node.js for backend?) that interact with AI models via RESTful or GraphQL APIs.
Deployment & Optimization: Deploy, monitor, and optimize AI-driven applications using containerization (Docker, Kubernetes) and cloud services (Azure).
Agentic Workflows: Implement Retrieval Augmented Generation (RAG) systems to enhance agent knowledge and accuracy.
Contribute hands on engineering work, including building and enhancing services, integrations, and automation supporting enterprise platforms.
Validate integrations and data flows between systems such as ERP, OMS, WMS, Planning tools, and Finance platforms (REST APIs, events, batch jobs).
Participate in feature delivery, production validation, and post deployment support as needed.
Identify, document, and track defects; collaborate with engineering teams on root cause analysis and resolution.
Support release planning and execution, including smoke testing, sanity checks, and rollback validation.
Contribute to architecture evolution, security by design, and cost efficient solutions.
Mentor engineers through pairing, feedback, and knowledge sharing.