AI Engineer

Salary: RM 8,000 – RM 10,000 per month

Role Overview

We are looking for a hands-on AI Engineer to design, develop, deploy and operate AI-powered applications and agent-based solutions on AWS. The role focuses on Generative AI, foundation models, AI agents, RAG, tool/API integration and cloud-native deployment. Experience with Amazon Bedrock AgentCore is strongly preferred.

Key Responsibilities

• Build practical Generative AI and agentic applications such as enterprise chatbots, knowledge assistants, customer support agents, IT support agents, document assistants and workflow automation agents.

• Design and implement AI agents using Amazon Bedrock and preferably Amazon Bedrock AgentCore, including model integration, tools, APIs, memory/context and observability where appropriate.

• Develop RAG solutions integrating enterprise knowledge, documents, databases and vector stores such as pgvector.

• Integrate AI agents with APIs, MCP servers, business systems, databases and other tools to enable useful actions and workflows.

• Develop application services using Python, TypeScript, Go, JavaScript or similar languages and frameworks.

• Deploy AI applications and supporting AWS services using secure, scalable and cost-effective cloud architectures.

• Automate infrastructure and application deployment using AWS CDK, CloudFormation, SAM, Terraform, CI/CD pipelines, scripts or equivalent approaches.

• Troubleshoot application, AI model, AWS, IAM, API and deployment issues and support solutions through development, testing and production.

• Apply appropriate security, reliability, monitoring, responsible AI and operational practices.

• Leverage AI-assisted development tools such as AWS Kiro, Amazon Q Developer, GitHub Copilot, ChatGPT or Claude while maintaining engineering quality.

AWS / AI Technology Scope

• Amazon Bedrock, Amazon Bedrock AgentCore, AWS Lambda, API Gateway, ECS, S3, DynamoDB, RDS, CloudWatch, IAM, EventBridge, Step Functions, vector databases, MCP, LangGraph and other relevant AI/cloud technologies

Example Solutions

• Enterprise AI Assistant – chat interface → API → AgentCore → Bedrock model → RAG/knowledge → response.

• Customer Support Agent – AI agent → knowledge base → business APIs/tools → recommended or automated action.

• IT Support Agent – employee request → agent → MCP/API tools → IT systems → diagnosis and resolution.

• Document Intelligence – document → S3 → AI processing → extraction/summarization/analysis → user.

• Workflow Automation Agent – user request → agent → tool selection → AWS/enterprise services → workflow result.

Required Skills

• Hands-on software/application engineering experience with the ability to design, build and deploy working solutions.

• Practical experience with AWS and cloud application deployment.

• Understanding of Generative AI, LLMs, prompt engineering, AI agents and agent orchestration.

• Experience with Amazon Bedrock or an equivalent LLM platform; hands-on AgentCore experience is strongly preferred.

• Experience with REST APIs, SDKs, authentication and integration with external or enterprise systems.

• Experience with RAG, embeddings, vector search or knowledge retrieval is preferred.

• Proficiency in at least one relevant programming language such as Python, TypeScript/JavaScript, Go or Java.

• Understanding of Infrastructure as Code, deployment automation and Git-based development practices.

• Ability to troubleshoot technical issues independently and explain architecture and technical trade-offs clearly.

Education

Bachelor's degree or equivalent practical experience in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science or a related discipline.

Preferred Qualifications

• Hands-on experience with Amazon Bedrock AgentCore and production GenAI/agentic applications.

• Experience with LangGraph, MCP, FastAPI, pgvector or similar agent/tool/RAG technologies.

• Experience with AWS CDK, Terraform, CloudFormation, SAM or CI/CD pipelines.

• Experience with Docker/ECS, Lambda, API Gateway, CloudWatch and AWS IAM.

• Experience with AI application evaluation, observability, guardrails, security and cost/latency optimization.

• Relevant AWS certifications such as AWS Certified AI Practitioner, Machine Learning Engineer – Associate, Solutions Architect or Developer..

Good to Have

• Hands-on engineering mindset with the ability to turn an AI concept into a working deployed solution.

• Strong practical understanding of AI agents, LLM applications and AWS.

• Ability to make pragmatic technical decisions under time constraints.

• Strong troubleshooting and problem-solving capability.

• Ability to use modern AI development tools effectively without relying on them blindly.

• Clear communication of architecture, implementation and trade-offs.

What We Offer:

• Attractive and competitive remuneration package: Salary, On-Target Incentive, Bonus and other benefits (Parking, Medical, Travel etc).

• Exam Certifications sponsored by the company.

• Certificate Incentive Programme.

• Young, creative and flexible working environment and culture.

• Long-term career growth or progression in career life.

• On the job training will be provided.