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.
This is a real engineering seat, not a shadowing placement. You will join our R&D team and take over live work, running data pipeline and a facial-recognition module that is deployed on active customer sites.
Most of your time is Python. You will maintain and extend an ingestion pipeline that moves data from AWS S3 through Redis Streams and MQTT into PostgreSQL, and you will own a computer-vision script that pulls frames from IP cameras over RTSP and runs them through a face-recognition pipeline. That script currently has a latency and detection problem we would like you to help solve.
About one day a week you will be on site with our engineers — HDB blocks, switch rooms, rooftops — commissioning and troubleshooting the systems your code runs on. Engineers who have watched their own software fail in a switch room write noticeably better software afterwards.
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Gain hands-on experience building applications using modern AI-driven software engineering practices, including Generative AI, Agentic AI, and AI-assisted development workflows.
Develop new features and enhance existing applications using modern programming languages, frameworks, and cloud technologies.
Participate in coding, testing, debugging, and troubleshooting activities, while gaining hands-on experience with engineering best practices
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Develop and refine AI-enabled workflows, including prompt design, LLM integration, and retrieval augmented generation (RAG) pipelines.
Support the full lifecycle of end-to-end software and AI implementation from ideation and proof-of-concept to testing, deployment, and continuous improvement.
Stay curious about emerging AI and software development trends, sharing insights and experimental findings with the wider team.
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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.
Participate in an in-house engineering squad for engineering excellence in design, development and operational of the Singapore Government Commercial Cloud Platform.
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Develop and refine AI-enabled workflows, including prompt design, LLM integration, and retrieval augmented generation (RAG) pipelines.
Support the full lifecycle of end-to-end software and AI implementation from ideation and proof-of-concept to testing, deployment, and continuous improvement.
Stay curious about emerging AI and software development trends, sharing insights and experimental findings with the wider team.
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Provide advanced support and troubleshooting for data pipelines, ETL/ELT processes, and data integration workflows across cloud and on-prem environments.
Enhance and mentor others in organizational, communication, and analytical skills, fostering a collaborative and data-driven engineering culture.
Gain exposure to the end-to-end machine learning lifecycle, from experimentation to production, by enabling robust and scalable data access for model training and inference.
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Quality Assurance & Testing: Prepare test cases, execute data quality validations, identify anomalies, and support bug resolution.
Technical Documentation: Author and maintain clear project documentation, data mapping specs, and system guides.
Deployment & Knowledge Transfer: Support system deployments, assist in preparing user training materials, and participate in client onboarding activities.
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