Job Opportunities API

SimplifyNext empowers organizations to thrive by simplifying complexities and unlocking next-generation technologies. Trusted by 150+ organizations, we drive measurable business impact through intelligent automation, AI-driven innovation, and next-gen applications.  With a team of more than 200 professionals, we operate from our headquarters in Singapore and offices in Thailand and Malaysia, serving clients across Asia Pacific and the Middle East. Recognized among Singapore’s 100 Fastest Growing Companies and Asia Pacific High-Growth Companies from 2023 to 2025. We partner with leading global technology providers, including Microsoft, AWS, ServiceNow, UiPath, Automation Anywhere, SS&C Blue Prism, OutSystems, Workato, ABBYY, etc. Our customers span leading global and regional organizations across industries such as Banking & Financial Services, Healthcare, Education, Manufacturing, Real Estate, Logistics, Transportation, Technology, Public Sector, and Defense. We’re not hiring someone to run models. We’re hiring someone who builds systems that think. 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. 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. What You'll Do 1. Build Agentic AI Systems Design and build sophisticated AI agents capable of independent operation, complex decision-making, and self-correction across domain-specific contexts. Develop orchestration workflows using frameworks such as LangChain, LangGraph, and/or the Microsoft Bot Framework to create robust conversational and task-oriented agents. Implement Retrieval-Augmented Generation (RAG) systems that connect agents to live, accurate knowledge bases — reducing hallucinations and improving output quality. Build Memory, Reasoning, and Planning (MRP) capabilities so agents can maintain context, reason across information, and execute multi-step plans. Design Agent-to-Agent (A2A) communication protocols that allow multiple autonomous agents to collaborate, delegate tasks, and exchange information securely. Work with LLM serving solutions (Ollama, vLLM) to ensure efficient, scalable inference in production environments. 2. Deploy and Operate at Scale Deploy and manage AI systems on major cloud platforms — AWS, Azure, or GCP — ensuring high availability, security, and scalability. Containerise applications with Docker and orchestrate deployments with Kubernetes, including end-to-end on-premise cluster setup and management. Build and maintain CI/CD pipelines using tools like Argo (Argo Workflows, Argo CD) for reliable, automated delivery of AI services. Expose AI capabilities through well-documented, performant APIs that integrate seamlessly with client-facing systems and applications. 3. Train and Fine-tune Models 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. Run structured experiments — hyperparameter tuning, ablations, model evaluation — to hit performance targets with confidence. Stay current with the latest developments in large language models and agentic AI, and bring new techniques into our systems proactively. Work across diverse data modalities: text, image, time-series, and graph-structured data. Who You Are Must-Have Production mindset — you’ve shipped AI systems to real users, not just demos. Strong fundamentals in agentic AI