AI Engineer

2 weeks ago

Kuala Lumpur, Kuala Lumpur, Malaysia AMK Technology Sdn Bhd Full time 80,000 - 120,000 per year

Key Responsibilities

1. AI Use Case Development

  • Collaborate with business teams to identify and prioritize high-impact AI use cases.
  • Design and implement AI workflows using LLMs and architectures such as RAG, tools-based agents, or prompt chains.
  • Prototype and deliver solutions using platforms such as
    OpenAI
    ,
    Azure OpenAI
    ,
    Claude
    , or
    Gemini
    .

2. Solution Architecture & Integration

  • Apply modern AI solution patterns (e.g., simple LLM apps, RAG pipelines, multi-agent systems).
  • Build APIs, middleware, or front-end applications to integrate AI capabilities into business systems.
  • Utilize
    vector databases
    (Pinecone, FAISS, Weaviate) for semantic search and retrieval.
  • Design scalable, enterprise-aligned solutions following
    cloud best practices
    .

3. Full-Stack Development

  • Develop responsive
    front-end applications
    (React, , or similar) for AI-powered interfaces.
  • Implement
    back-end services
    , Python, FastAPI) to orchestrate AI workflows.
  • Ensure
    secure authentication
    ,
    role-based access
    , and
    seamless UX
    for end users.

4. Prompt Engineering & Optimization

  • Design, test, and optimize prompts for effective LLM behavior.
  • Apply techniques such as
    few-shot learning
    ,
    chain-of-thought prompting
    , and
    function calling
    .

5. Orchestration & Automation

  • Build modular, composable pipelines using
    LangChain
    ,
    LlamaIndex
    ,
    Semantic Kernel
    , or similar frameworks.
  • Develop orchestration workflows that integrate with open-source and enterprise AI tools.

6. Testing, Evaluation & Monitoring

  • Evaluate AI systems using metrics such as
    accuracy
    ,
    hallucination rate
    , and
    latency
    .
  • Continuously monitor performance and improve systems based on
    user feedback
    and
    business outcomes
    .

7. Cloud Infrastructure & Deployment

  • Deploy AI solutions on
    AWS
    ,
    Azure
    , or
    GCP
    .
  • Utilize
    Docker
    and
    Kubernetes
    for containerized, scalable services.
  • Implement
    DevOps/MLOps practices
    for CI/CD, observability, and system reliability.

8. Documentation & Best Practices

  • Maintain documentation for AI workflows, integrations, and architecture.
  • Promote
    reusable components
    ,
    prompt templates
    , and
    AI architecture standards
    .
  • Share insights, best practices, and learnings across the organization.

Top 5 Skills / Experience

Technical Skills

  • Proven
    full-stack development
    experience (front-end and back-end).
  • Hands-on expertise in
    AI solutioning
    ,
    LLMs
    ,
    RAG pipelines
    , and orchestration frameworks.
  • Strong proficiency in
    cloud platforms
    (AWS, Azure, GCP) and containerized deployments.
  • Deep understanding of
    APIs
    ,
    microservices
    , and
    system integration
    .
  • Experience with
    vector databases
    (Pinecone, FAISS, Weaviate) and
    semantic search
    .
  • Demonstrated portfolio or GitHub showcasing AI-related projects.
  • Passion for
    emerging technologies
    and proactive learning.

Soft Skills

  • Strong analytical and problem-solving skills.
  • Excellent communication; able to translate complex technical ideas into business outcomes.
  • Self-motivated, collaborative, and adaptable in fast-paced environments.

Preferred Qualifications

  • Bachelor's Degree (Honours) in
    Computer Science
    ,
    Information Technology
    , or a related field recognized by the government.
  • Solid understanding of key
    software design principles
    and
    enterprise architecture
    .

Experience Requirements

  • Senior Developer:
    Minimum
    4+ years of experience
    , including at least
    5 years in automation development
    and prior experience leading technical projects.

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