Overview
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Job-type |
Full-Time |
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Industries |
|
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Salary |
MYR 12,000
- 17,000
/Month
|
Who you'll be working for
The client is an international supplier of broadcast management systems.
What requirements you'll need to be eligible
Required Skills & Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or related field.
- 5+ years of experience in software engineering, AI engineering, or machine learning development, with strong exposure to production-grade LLM systems.
- Proven experience leading AI initiatives, technical teams, or enterprise AI solution delivery.
- Strong proficiency in Python and AI orchestration frameworks such as LangChain, LangGraph, CrewAI, Agno, AutoGen, or equivalent.
- Deep understanding of LLM architectures, prompt engineering, agentic workflows, tool calling, memory systems, and RAG implementations.
- Experience with vector databases such as Pinecone, FAISS, Weaviate, or ChromaDB.
- Strong knowledge of RESTful APIs, microservices architecture, and enterprise integration patterns.
- Experience deploying and managing AI workloads using Azure OpenAI, OpenAI APIs, self-hosted LLMs, or open-source models such as Mistral and Llama.
- Familiarity with Docker, Kubernetes (AKS), CI/CD pipelines, and cloud-native deployment practices.
- Strong communication and stakeholder management skills with the ability to bridge technical and business discussions effectively.
- Experience working in agile and cross-functional product environments.
Preferred Qualifications
- Experience building and managing multi-agent AI ecosystems involving planning, delegation, reflection, and autonomous task execution.
- Strong understanding of LLMOps, observability, evaluation frameworks, embedding optimization, and AI performance monitoring.
- Experience implementing enterprise AI governance, explainability, and compliance practices.
- Knowledge of AI security, sandboxing, rate limiting, and safe execution strategies for autonomous systems.
- Microsoft Azure AI, OpenAI, or cloud AI certifications are an advantage.
- Experience mentoring engineering teams and driving AI capability development within organizations.
What you'll be doing on the job
We are looking for a strategic and hands-on AI Lead to drive the design, development, and delivery of next-generation AI solutions powered by Large Language Models (LLMs), agentic frameworks, and autonomous AI systems.
This role combines technical leadership, solution architecture, and team mentorship. You will lead the AI engineering function in building scalable, production-ready AI agents and intelligent workflows that enhance customer experiences, automate operations, and unlock business insights.
You will work closely with product, engineering, and business stakeholders to define AI strategy, establish best practices, and oversee the implementation of enterprise-grade AI systems using frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI, Agno, and related technologies.
Key Responsibilities
- Lead the architecture, development, and deployment of AI-powered applications and autonomous agent systems.
- Design scalable multi-agent workflows using frameworks such as LangGraph, CrewAI, Agno, AutoGen, or similar orchestration platforms.
- Define technical standards and best practices for prompt engineering, memory management, agent coordination, observability, and evaluation.
- Guide the development of composable AI workflows integrating APIs, enterprise systems, vector databases, and external tools securely and efficiently.
- Collaborate with product owners and business stakeholders to identify high-impact AI use cases and translate them into measurable solutions.
- Oversee the integration of AI agents into enterprise platforms, including chatbots, workflow automation systems, analytics tools, and internal knowledge platforms.
- Establish AI governance practices including guardrails, human-in-the-loop workflows, auditability, safety constraints, and responsible AI implementation.
- Drive AI innovation initiatives by evaluating emerging LLMs, frameworks, and AI tooling across cloud and open-source ecosystems.
- Mentor and lead AI engineers by providing technical guidance, architecture reviews, and hands-on support throughout the development lifecycle.
- Work closely with DevOps and infrastructure teams to ensure scalable deployment, monitoring, and operational excellence for AI systems.
- Define KPIs and performance benchmarks for AI solutions, ensuring reliability, accuracy, scalability, and business impact.
Consultant Contact
Sound interesting?
Apply!
