Ibroad Solutions LLP
Software Engineer
Designs, builds, and deploys Generative AI chatbots, copilots, and Agentic AI solutions, integrating LLMs with enterprise applications, APIs, databases, and CRM/ERP systems. Core stack: Python, RAG/vector search, commercial and open-source LLMs, and agent frameworks like LangChain and LangGraph.
- Compensation
- ₹50K–₹100K / yr
- Location
- Malaysia
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Software engineering
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, AI/ML, Software Engineering, or related field
- 3+ years of experience in AI/ML engineering, software engineering, or related role
- Hands-on experience developing LLM-powered applications, AI chatbots, or Generative AI solutions
- Practical experience with AI agents, Agentic AI, tool/function calling, or AI workflow automation
- Experience with open-source LLMs such as Llama, Qwen, Mistral, Gemma, or equivalent
- Strong Python programming and software engineering skills
- Experience with REST APIs, system integration, databases, and cloud/on-premise environments
- Hands-on experience with RAG, vector databases, embeddings, and prompt engineering
- Experience with AI/LLM frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent
- Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, Gemini, and/or open-source models
- Familiarity with Docker, Git, CI/CD, and production deployment
Nice to have
- Experience deploying/serving open-source LLMs using vLLM, Hugging Face, Ollama, or equivalent
- Experience with MCP, multi-agent systems, LLM evaluation/observability, fine-tuning, LoRA/QLoRA, or model optimization
- Experience with AWS, Azure, or Google Cloud
- Understanding of AI security, data privacy, access control, and responsible AI
Full description
- Role: AI Engineer – Gen AI, Agentic AI & Integration
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- Location: Petaling Jaya, Selangor, Malaysia
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- Job Overview
- We are looking for a hands-on AI Engineer to design, develop, integrate, and deploy Generative AI, AI chatbot, and Agentic AI solutions. The role will focus on applying LLM technologies to real business use cases and integrating AI capabilities into our existing applications, APIs, databases, and enterprise systems.
- Key Responsibilities
- Design and develop AI-powered chatbots, copilots, and LLM applications for business use cases.
- Build Agentic AI solutions capable of reasoning, tool calling, multi-step task execution, and workflow automation.
- Implement RAG, embeddings, vector search, prompt engineering, function/tool calling, and conversational memory.
- Work with both commercial and open-source LLMs, selecting models based on performance, cost, security, and deployment requirements.
- Integrate AI models and agents with existing applications, REST APIs, databases, CRM/ERP systems, and enterprise platforms.
- Develop AI integration services and APIs that enable existing systems to consume AI capabilities and allow agents to perform authorized business actions.
- Evaluate, monitor, and optimize AI solutions for accuracy, reliability, latency, scalability, security, and cost.
- Collaborate with software engineers, architects, product teams, and business stakeholders to identify and deliver practical AI solutions.
- Stay current with developments in Generative AI, open-source LLMs, Agentic AI, and AI automation.
- Requirements
- Bachelor's degree in Computer Science, AI/ML, Software Engineering, or related field.
- 3+ years of experience in AI/ML engineering, software engineering, or a related role.
- Proven hands-on experience developing LLM-powered applications, AI chatbots, or Generative AI solutions.
- Practical experience with AI agents, Agentic AI, tool/function calling, or AI workflow automation.
- Experience with open-source LLMs such as Llama, Qwen, Mistral, Gemma, or equivalent.
- Strong Python programming and software engineering skills.
- Experience with REST APIs, system integration, databases, and cloud/on-premise environments.
- Hands-on experience with RAG, vector databases, embeddings, and prompt engineering.
- Experience with AI/LLM frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent.
- Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, Gemini, and/or open-source models.
- Familiarity with Docker, Git, CI/CD, and production deployment.