AI Engineer

About this role

<div class="content-intro"><p>SimplifyNext is a Singapore-headquartered digital engineering firm, with offices in Thailand and Malaysia, working across three converged areas: agentic AI, AI-enabled application modernisation, and intelligent automation.<br>&nbsp;<br>We bring strong business process consulting together with deep technology skills and modern delivery practices – product-centric, agile and AI-assisted. We build bespoke applications and AI systems, and we go deep on the platforms our clients run on, including Microsoft, AWS, ServiceNow, Databricks and UiPath. That combination of engineering depth and platform depth is what our clients come to us for.<br>&nbsp;<br>We build and modernise the systems people depend on daily. Our 300+ practitioners are multi-disciplinary by design – business consultants, software engineers, architects, AI engineers and designers working on the same delivery, not handing off between silos. We focus on delivering outcomes for clients, building strong careers for our people, and staying ahead of the technology curve. We hire because we are growing.</p></div><p><strong><em>We’re not hiring someone to run models. We’re hiring someone who builds systems that think.</em></strong></p> <p>At SimplifyNext, our AI Engineers are core to how we deliver transformation — designing and deploying intelligent systems that genuinely change how organisations operate. You won’t be a supporting act to another team. You’ll be the one building the agents, pipelines, and infrastructure that make our AI products real.</p> <p>We work across public sector and enterprise, at the intersection of AI, automation, and product-led transformation. If you’re energised by hard engineering problems, care about production outcomes - not just research benchmarks - and want your work to reach real users at scale, read on.</p> <h3><strong>What You'll Do</strong></h3> <h4>1. Build Agentic AI Systems</h4> <ul> <li>Design and build sophisticated AI agents capable of independent operation, complex decision-making, and self-correction across domain-specific contexts.</li> <li>Develop orchestration workflows using frameworks such as LangChain, LangGraph, and/or the Microsoft Bot Framework to create robust conversational and task-oriented agents.</li> <li>Implement Retrieval-Augmented Generation (RAG) systems that connect agents to live, accurate knowledge bases — reducing hallucinations and improving output quality.</li> <li>Build Memory, Reasoning, and Planning (MRP) capabilities so agents can maintain context, reason across information, and execute multi-step plans.</li> <li>Design Agent-to-Agent (A2A) communication protocols that allow multiple autonomous agents to collaborate, delegate tasks, and exchange information securely.</li> <li>Work with LLM serving solutions (Ollama, vLLM) to ensure efficient, scalable inference in production environments.</li> </ul> <h4>2. Deploy and Operate at Scale</h4> <ul> <li>Deploy and manage AI systems on major cloud platforms — AWS, Azure, or GCP — ensuring high availability, security, and scalability.</li> <li>Containerise applications with Docker and orchestrate deployments with Kubernetes, including end-to-end on-premise cluster setup and management.</li> <li>Build and maintain CI/CD pipelines using tools like Argo (Argo Workflows, Argo CD) for reliable, automated delivery of AI services.</li> <li>Expose AI capabilities through well-documented, performant APIs that integrate seamlessly with client-facing systems and applications.</li> </ul> <h4>3. Train and Fine-tune Models</h4> <ul> <li>Train, fine-tune, and optimise custom AI models using TensorFlow and/or PyTorch, particularly where off-the-shelf models don’t meet specific project requirements.</li> <li>Run structured experiments — hyperparameter tuning, ablations, model evaluation — to hit performance targets with confidence.</li> <li>Stay current with the latest developments in large language models and agentic AI, and bring new techniques into our systems proactively.</li> <li>Work across diverse data modalities: text, image, time-series, and graph-structured data.</li> </ul> <h3><strong>Who You Are</strong></h3> <p><strong>Must-Have</strong></p> <ul> <li>Production mindset — you’ve shipped AI systems to real users, not just demos.</li> <li>Strong fundamentals in agentic AI — you understand how to design agents that are reliable, not just impressive.</li> <li>Hands-on with at least one major cloud platform (AWS, Azure, or GCP) for deploying and managing AI workloads.</li> <li>Solid Python engineering skills and comfort working in a Git-based, collaborative codebase.</li> <li>Experience with containerisation (Docker) and orchestration (Kubernetes) in real deployment contexts.</li> <li>Clear communicator — able to explain complex technical decisions to non-technical stakeholders.</li> </ul> <p><strong>Good to Have</strong></p> <ul> <li>3–7 years in AI/ML engineering or a closely related role (data engineering, MLOps, applied research).</li> <li>Hands-on experience with LangChain, LangGraph, Ollama, vLLM, or similar orchestration and serving tools.</li> <li>Demonstrable RAG system implementation and optimisation experience.</li> <li>Familiarity with Memory, Reasoning, and Planning (MRP) concepts and Agent-to-Agent (A2A) protocol design.</li> <li>Experience with MLOps tooling, particularly Argo Workflows and Argo CD.</li> <li>Proficiency in TensorFlow and/or PyTorch for model training and fine-tuning.</li> <li>Exposure to knowledge graphs, semantic web technologies, or prompt engineering strategies.</li> </ul> <p><strong>This role is not for you if…</strong></p> <ul> <li>You’ve only worked with AI in sandboxed or prototype environments and haven’t dealt with production reliability challenges.</li> <li>You prefer to work from a detailed spec rather than figuring out the right approach as you go.</li> <li>You treat deployment as someone else’s problem</li> <li>You measure success by models trained, not