Lead the end-to-end delivery of production-ready enterprise AI solutions powered by Large Language Models (LLM), Retrieval-Augmented Generation (RAG) and agent-based workflows - owning solution architecture, driving hands-on delivery, and serving as the senior technical point of contact for customers.
This is a hands-on leadership role: the person both builds and leads. They take solutions from proof-of-concept to stable production, set delivery and engineering standards, mentor the team, and turn each engagement into reusable capability that scales across multiple customers and use cases.
Responsibilities
- Solution design & architecture
- Translate customer requirements into practical, scalable solution architectures, workflows and delivery plans.
- Own the technical design of AI solutions — knowledge bases, RAG pipelines, agent and workflow automation, authentication and system integration.
- Select models, frameworks and configurations based on quality, latency, cost, security and business requirements.
- Design modular, reusable AI capabilities that can be applied across multiple customers and use cases.
- Delivery leadership
- Lead delivery from proof-of-concept through to production and continuous optimisation, ensuring quality, security and timeliness.
- Mentor and review the work of the AI Solutions Engineer(s); set engineering standards and best practices.
- Establish AI evaluation, automated testing, logging and monitoring; drive optimisation of prompts, workflows and model choices.
- Plan effort, scope and priorities; manage technical risks and dependencies.
- Customer & stakeholder engagement
- Act as the senior technical lead in customer discussions, demonstrations, proof-of-concepts and implementation workshops (in Bahasa Melayu and English).
- Translate business goals into solutions and advise customers on scope, feasibility, delivery sequencing and effort estimates.
- Communicate effectively across management, business teams and technical teams.
- Integration, operations & governance
- Oversee integration with customer systems — APIs, databases, messaging channels and enterprise platforms (e.g. CRM / billing).
- Address accuracy, hallucination, latency, cost and system-stability issues across the solution lifecycle.
- Support LLMOps and software-engineering practices: version control, testing, CI/CD, monitoring, logging and security review.
- Ensure solutions meet security, data-privacy, access-control, explainability and audit requirements (PDPA and sector regulations).Requirements
- Education
- Degree in Computer Science, AI, Software Engineering, Information Technology, Data Science or a related discipline.
- Experience
- Around 2–3 years of hands-on software / AI delivery experience, including production LLM / RAG / agent solutions delivered from proof-of-concept to production.
- Demonstrated experience leading delivery or mentoring engineers, ideally in a customer-facing setting.
- Software engineering
- Strong Python and software-engineering fundamentals.
- Experienced with APIs, databases, backend development and system integration.
- Familiar with cloud platforms, Docker, Git, CI/CD and monitoring.
- Hands-on AI expertise
- Strong command of mainstream large language models and model selection.
- Skilled in prompt engineering, structured output and tool calling.
- Experienced in RAG, vector search and knowledge-base development.
- Able to design and build AI agents and automated workflows.
- Familiarity with multimodal AI (documents, images, OCR, voice / audio) is an advantage.
- Experience with platforms such as GPTBots.ai, Dify, LangChain or LlamaIndex.
- Comfortable using Claude Code and AI-powered IDEs to accelerate delivery.
- Production delivery
- Proven ability to take solutions to production and resolve accuracy, hallucination, latency, cost and stability issues.
- Familiar with AI evaluation, automated testing, logging and continuous optimisation.
- Business understanding & communication
- Able to translate business requirements into practical AI solutions.
- Able to communicate clearly and credibly with management, business teams and technical teams.
- Language
- Bahasa Melayu — mandatory (spoken and written, professional).
- English — mandatory (spoken and written, professional).
- Chinese — an advantage, not required.
- Behavioural Competencies
- Strong analytical, troubleshooting and problem-solving skills.
- Ability to translate business requirements into practical, maintainable technical solutions.
- Strong ownership, accountability and attention to delivery quality.
- Good communication, presentation, documentation and cross-functional collaboration skills.
- Fast learner with a proactive, adaptable, hands-on mindset and a genuine interest in AI.
- Ideal Candidate Profile
- A senior engineer who is not only fluent in AI models, but can also architect and integrate systems, solve real production issues, lead a small delivery team, understand business goals, and communicate clearly across technical and non-technical teams.Required SkillsPythonDevOps (Docker / Kubernetes / CI-CD)
Pay: RM6,000.00 - RM7,000.00 per month
Benefits:
- Opportunities for promotion
- Professional development
Work Location: In person