Job SummaryWe 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 building practical AI applications and integrating LLM capabilities with existing applications, APIs, databases, and enterprise systems.Key ResponsibilitiesDesign and develop AI chatbots, copilots, and LLM-powered applications.Build Agentic AI solutions with reasoning, tool/function calling, multi-step task execution, and workflow automation.Implement RAG, embeddings, vector search, prompt engineering, and conversational memory.Work with commercial and open-source LLMs and select appropriate models based on business and technical requirements.Integrate AI solutions with REST APIs, databases, CRM/ERP systems, and enterprise applications.Develop AI integration services and APIs for existing systems and business workflows.Evaluate and optimize AI solutions for accuracy, reliability, latency, scalability, security, and cost.Collaborate with software engineers, architects, product teams, and business stakeholders.Keep up to date with developments in Generative AI, LLMs, Agentic AI, and AI automation.RequirementsBachelor's degree in Computer Science, AI/ML, Software Engineering, or a related field.3+ years of experience in AI/ML engineering, software engineering, or a related role.Hands-on experience developing LLM 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 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.Preferred SkillsExperience 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.Knowledge of AI security, data privacy, access control, and responsible AI.
Conversion to Permanent Position: Interns who excel at their tasks and are a good fit for the company will be offered a permanent position after the internship period in their desired field.
Position yourself for future engineering roles by owning small subsystems and seeing work progress from prototype to production.
Ready to turn classroom ideas into working machines? By working with us at FlexLink Engineering Sdn Bhd, you'll join a hands-on engineering team that designs automation modules and production systems for regional manufacturers, improving how products are made and moved.
As a Mechanical Engineering Intern, you will help build prototypes, test rigs and component assemblies that move projects from concept to shop floor. You will take part in design reviews, laboratory testing, component sourcing and system integration to scale ideas into manufacturable solutions.
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Maintain and harden existing sites — firmware, switch configs, IP documentation, rack hygiene, cable labelling, replacing failing routers and APs with minimal store downtime.
Reduce repeat work — when the same fault appears at three stores, you raise it, script around it or propose the permanent fix.
Support endpoints — in-store PCs, terminals and printers, remote support via SSH / RDP / RustDesk / MeshCentral / Intel AMT.
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Maintain and harden existing sites — firmware, switch configs, IP documentation, rack hygiene, cable labelling, replacing failing routers and APs with minimal store downtime.
Reduce repeat work — when the same fault appears at three stores, you raise it, script around it or propose the permanent fix.
Support endpoints — in-store PCs, terminals and printers, remote support via SSH / RDP / RustDesk / MeshCentral / Intel AMT.
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Mentor engineers through code reviews, pair programming, and documentation to raise team standards and reduce defects. Set coding conventions, run knowledge-sharing sessions, and help junior engineers take ownership of operational tasks.
Build a visible portfolio of production systems by driving deployments, monitoring, and structured postmortems that show operational thinking. Own service-level metrics, alerting, and incident follow-up, and present outcomes to product and client stakeholders.
Accelerate your path toward senior solutions engineering or product roles by owning measurable demo success metrics. Document technical wins and case studies that highlight your impact on deals and deployments.
Gain visibility with product and engineering teams by feeding back field insights that influence roadmap priorities. Learn how product decisions are made and how to translate customer requests into deliverable scope.
Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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