Full Time AI Solutions Developer Jobs, in ISoftstone Malaysia Shah Alam - Maukerja

Job Summary

Tapestry Supply Chain IT is seeking a hands-on AI Developer with technical expertise across generative AI frameworks and orchestration platforms (e.g., Dify, Microsoft Copilot Studio, Azure OpenAI, Azure AI Services). In this role, you will design, build, test, and deploy production-grade AI solutions, intelligent agents, and RAG pipelines to optimize Supply Chain and Enterprise operations.

You will work closely with Business Analysts to translate functional requirements into high-performing, scalable AI applications. The ideal candidate thrives on writing code, optimizing prompts, integrating APIs, and taking technical ownership of AI applications across their full deployment lifecycle.

Key Responsibilities

1. AI Opportunity Exploration

• Partner with Business Analysts and business stakeholders to understand business challenges and surface AI opportunities.
• Assess technical feasibility and implementation complexity for proposed AI use cases.
• Identify suitable open AI platforms, models, and tools to address each business need (e.g., Dify, Copilot Studio, Azure OpenAI, Azure AI Services, Claude, or comparable technologies).
• Design, build, and deploy production-ready AI solutions, including Retrieval-Augmented Generation (RAG) systems, multi-agent workflows, custom chatbots, and document processing applications.
• Write clean, maintainable backend code (Python/TypeScript) and build custom API connectors to bridge AI engines with enterprise ERP, PLM, and Supply Chain databases.

2. AI Proof of Concept (POC) Development

• Develop rapid prototypes and Proof of Concepts to validate solution feasibility.
• Implement advanced RAG techniques such as semantic chunking, hybrid search, hybrid re-ranking, and vector database indexing to minimize hallucinations and maximize response accuracy
• Architect, test, and refine system prompts, agentic workflows, and tool-calling parameters to optimize accuracy and performance across enterprise LLMs (e.g., GPT-4o, Claude, Qwen).
• Evaluate and benchmark AI model output quality, latency, token costs, and context limits to select the optimal model for each use case.
• Demonstrate POCs together with the BA to business stakeholders and iterate based on feedback.

3. Integrations & Deployment

• Containerize and deploy AI microservices into Tapestry's Enterprise QA and Production cloud environments (Azure), in accordance with required Solution Architecture, InfoSec, and Legal approval gates prior to any Production release.
• Partner with the Solution Architecture team to review vendor technical deliverables and confirm deployment quality aligns with business requirements and Tapestry's security/compliance expectations.
• Configure authentication, access controls, data encryption, and network security standards across all AI endpoints in line with InfoSec requirements.
• Collaborate with DevOps, Cloud Infrastructure, and IT Security teams to build secure CI/CD pipelines, incorporating mandatory approval checkpoints before any Production release.
• Prepare deployment documentation and compliance evidence required to support InfoSec and Legal sign-off.
• Support user acceptance testing and business adoption activities alongside the BA and vendor.

4. Testing, Optimization & Maintenance

• Execute unit testing, regression testing, and automated evaluation scripts to monitor accuracy, drift, and performance metrics over time.
• Monitor live applications, conduct root-cause triage for bugs or incorrect outputs, and issue performance patches.
• Create comprehensive technical documentation, including API schemas, system workflow diagrams, and runbooks.
• Identify opportunities for iteration or expansion of existing AI solutions based on user feedback and usage patterns.

5. Technology Awareness & Knowledge Sharing

• Stay current on open AI platforms, industry trends, and emerging enterprise AI capabilities.
• Provide guidance on responsible AI usage, data privacy, and security considerations for proposed use cases.
• Contribute reusable POC patterns, prompt templates, and lessons learned to a shared team knowledge base.

Required Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or related field.
• 5+ years of experience in software development, digital transformation, or AI-related roles.
• 2+ years of hands-on experience delivering AI, Generative AI, LLM, Copilot, chatbot, RAG, or intelligent automation solutions.
• Strong programming proficiency in Python or TypeScript/JavaScript.
• Hands-on experience with LLM orchestration global platforms (e,g, Dify, Microsoft Copilot Studio, Claude, Qwen and comparable technologies).
• Familiarity with LLM technologies and AI application patterns, including Retrieval-Augmented Generation (RAG), AI Agents, Knowledge Management, Document Intelligence, and Conversational AI.
• Experience evaluating business requirements and translating them into working AI prototypes.
• Hands-on experience building and consuming RESTful APIs, Webhooks, and microservices to integrate AI components with enterprise systems
• Production experience with Azure OpenAI Services, Azure AI Search, and enterprise Cloud platforms.
• Experience coordinating with external vendors or implementation partners for solution deployment.
• Strong verbal and written English communication skills.

Preferred Qualifications

• Experience with global open AI platforms or frameworks: e.g. Claude. Qwen, or comparable LLM orchestration tools.
• Experience building data/API integrations with Supply Chain or Enterprise systems (i.e. Snowflake, A360, ERP, PLM system)
• Knowledge of containerization tools and CI/CD deployment pipelines.
• Knowledge of AI governance, data privacy, and security considerations.
• Experience delivering AI projects in multinational organizations.

Soft Skills

• Technical Problem Solver: A pragmatic, hands-on developer who enjoys debugging complex LLM workflows and writing efficient code.
• Agile Execution: Thrives in rapid iteration cycles, turning user stories into functional code quickly.
• Clear Communicator: Able to explain complex technical constraints and AI trade-offs clearly to BAs and non-technical stakeholders.
• Comfortable working in ambiguous and fast-evolving AI environments.

Success Measures (First 6 Months)

• Successfully evaluate and prioritize AI opportunities across Supply Chain functions.
• Successfully build and deploy at least 2 production-ready AI applications or agent workflows into Tapestry’s production environment.
• Achieve and maintain performance accuracy and low latency targets across deployed RAG pipelines.
• Establish robust automated testing/evaluation frameworks for monitoring live LLM response quality.
• Deliver clean, reusable codebases and API integrations with complete technical runbook documentation.
• Build a reusable library of POC patterns and prompt templates for future AI use cases.