AI Solutions Engineer
Singtel is hiring an AI Solutions Engineer in Singapore, Singapore. Level rates it ; you can apply on Singtel.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- The posting mentions OpenAI, LangChain and LlamaIndex among its tools and skills.
- Level classes it as a mid-level role.
- Singtel has 496 other open roles on Level.
AI in this role
Passionate about making a real impact? Be at the forefront of the data centre (DC) industry with a unique focus on sustainability, connectivity and AI which sets us apart as the next generation DC operator. You will also get to gain invaluable experience in a fast-growing industry that is powering the digitalisation wave. Be empowered to co-create the future with our dynamic teams!”
We are seeking an AI Solutions Engineer (Agentic AI) to design, build and deploy enterprise AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and AI Agents. You will develop production-ready AI applications that automate knowledge-intensive workflows, integrate with enterprise systems and deliver secure, scalable and trustworthy AI experiences.
How You will Make An Impact:
AI Solution Development
- Design, develop and deploy AI agents using LLMs, RAG and prompt engineering.
- Build scalable AI workflows that automate enterprise business processes.
- Translate business requirements into practical AI solutions.
- Develop reusable prompt workflows, tool-calling capabilities and structured outputs.
Knowledge & RAG Engineering
- Build and optimise RAG pipelines connected to approved enterprise knowledge sources.
- Improve retrieval quality through chunking, embeddings, indexing and metadata strategies.
- Maintain trusted knowledge bases and ensure source-grounded AI responses.
AI Platform & Integration
- Integrate AI applications with enterprise systems, APIs, databases and internal platforms.
- Develop secure tool-calling capabilities and support deployment into production.
- Monitor and optimise AI application performance.
Model Quality & Governance
- Design evaluation frameworks to measure response quality, retrieval accuracy and hallucination risks.
- Optimise prompts, guardrails and model performance.
- Support governance, version control and human-in-the-loop review processes.
Stakeholder Collaboration
- Partner with product, engineering and business teams to deliver AI solutions.
- Support demonstrations, UAT, production rollout and technical documentation.
- Communicate technical concepts clearly to technical and non-technical stakeholders.
Skills for Success:
- Bachelor's Degree in Computer Science, Artificial Intelligence, Data Science or related discipline.
- 3–5 years of software engineering experience with Python.
- Hands-on experience building LLM applications, AI Agents or RAG solutions.
- Experience with LangChain, LangGraph, LlamaIndex or similar AI frameworks.
- Experience integrating APIs, databases and enterprise systems.
- Knowledge of vector databases, semantic search and prompt engineering.
- Experience with Git, CI/CD and container technologies.
Preferred Skills:
- Experience with Azure OpenAI, AWS Bedrock or Google Vertex AI.
- Knowledge of MCP (Model Context Protocol) or AI agent orchestration.
- Experience deploying open-source LLMs (e.g. vLLM, Ollama).
- Exposure to MLOps, model fine-tuning or domain adaptation.
How we rate this
AI Solutions Engineer at Singtel rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI agents, Fine-tuning, and ML Ops. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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