ZCloak.AI Hiring! AI Engineer in , Earn up to SGD 7,000 - Ricebowl

AI Engineer

Singapore · Full-time/Internship

About zCloak.AI

zCloak.AI helps enterprises transform business operations through production-ready AI workflows.

We build an AI agent work platform that connects enterprise data, tools, and teams with secure, governed AI execution.

The Role

We are looking for an AI Engineer to design, build, and improve the AI systems that power zCloak.AI.

You will work on production-grade AI agents, retrieval systems, tool-use infrastructure, agent memory, workflow orchestration, evaluation, and model integration. Your work will span the full AI application stack — from models and prompts to retrieval, tools, runtime systems, observability, and production reliability.

This is a hands-on engineering role. You will work closely with product, platform, and deployment teams to turn emerging AI capabilities into dependable product features that can operate inside real enterprise workflows.

You should be comfortable experimenting rapidly, measuring system behaviour, debugging complex failures, and turning successful prototypes into scalable and maintainable production systems.

What You Will Do

• Design, build, and maintain production-grade AI agents and agentic systems.
• Develop RAG pipelines, retrieval systems, document-processing pipelines, and knowledge-grounding mechanisms.
• Build and improve tool-use systems, agent memory, workflow orchestration, approval flows, and human-in-the-loop mechanisms.
• Integrate and evaluate foundation models from multiple providers and determine the appropriate model, prompting, routing, and execution strategy for different tasks.
• Develop structured-output, function-calling, and multi-step reasoning workflows for enterprise use cases.
• Build evaluation frameworks to measure task completion, accuracy, reliability, latency, and cost.
• Design guardrails, validation mechanisms, fallback strategies, and failure-recovery logic for AI systems.
• Develop tracing, logging, monitoring, and observability capabilities for agent execution.
• Investigate and resolve failures across models, prompts, retrieval, tools, data pipelines, application code, and infrastructure.
• Improve model and agent performance through prompt optimisation, retrieval improvements, model selection, context management, and system-level engineering.
• Build reusable AI components, internal libraries, SDKs, and platform capabilities that can support multiple products and customer deployments.
• Work with product and engineering teams to translate product requirements into scalable AI system designs.
• Evaluate new models, agent frameworks, research developments, and AI infrastructure, and determine where they can create practical product improvements.
• Contribute to technical architecture, engineering standards, testing practices, and system documentation.
• Participate in production support and incident investigation where AI system behaviour is involved.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
• Strong programming ability in Python.
• Solid software engineering fundamentals, including APIs, databases, testing, debugging, version control, and production software development.
• Hands-on experience building applications with LLMs, AI agents, tool calling, or workflow orchestration.
• Experience building RAG systems using embedding models, vector databases, retrieval techniques, and document-processing tools.
• Understanding of modern LLM application patterns, including prompting, structured outputs, function calling, context management, and model evaluation.
• Experience integrating external APIs, databases, and model providers.
• Experience working in Linux-based development or deployment environments.
• Ability to investigate ambiguous technical problems and systematically identify root causes.
• Ability to take an AI feature from experimentation through production deployment and ongoing improvement.
• Clear written and verbal communication skills in English.

Preferred Qualifications

• Professional experience building production AI applications, agent systems, machine learning systems, or developer platforms.
• Experience with one or more agent frameworks or SDKs, such as LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, OpenClaw, Hermes, or equivalent systems.
• Familiarity with MCP, agent memory, tracing, observability, evaluation, and prompt or model versioning.
• Experience designing multi-agent systems or long-running workflow orchestration.
• Experience building model evaluation pipelines, test datasets, benchmarks, or automated regression testing for AI systems.
• Familiarity with model routing, caching, context management, inference optimisation, and AI application cost optimisation.
• Experience with vector databases and retrieval infrastructure such as pgvector, Qdrant, Milvus, Pinecone, Weaviate, or similar systems.
• Familiarity with AWS, Google Cloud, Azure, containers, CI/CD, and infrastructure automation.
• Understanding of enterprise security concepts, including identity, permissions, secrets management, audit logs, and data governance.
• Experience working with document-heavy or workflow-heavy enterprise applications.
• Experience working in a startup or another fast-moving engineering environment.
• Contributions to open-source AI projects, relevant research, technical publications, or substantial deployed AI projects.

Candidates with relevant professional experience are preferred. Exceptional fresh graduates with strong AI engineering experience gained through internships, research, open-source contributions, competitions, or substantial deployed projects are also encouraged to apply.

What Success Looks Like

A successful AI Engineer can take an AI capability from an early experiment to a reliable production system.

You will be able to identify why an AI system fails, determine whether the problem comes from the model, prompt, retrieval, tools, data, orchestration, or surrounding application logic, and implement practical improvements.

You will build systems that become progressively more accurate, reliable, observable, efficient, and reusable, while helping zCloak.AI turn rapidly evolving AI capabilities into stable enterprise product features.

Apply

If you want to build AI systems that operate inside real enterprise workflows, we would like to hear from you.

Please send your CV and, where available, your GitHub profile, portfolio, technical writing, research, or examples of relevant projects to:

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Learn more about zCloak.AI:

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Email subject:
Application – [Position] – [Full Name] – [Experience/School] – [Earliest Start Date]

Pay: $1,000.00 - $7,000.00 per month

Benefits:

• Additional leave
• Flexible schedule
• Work from home

Education:

• Bachelor's or equivalent (Preferred)

License/Certification:

• Pass to work in Singapore (Required)

Location:

• Singapore (Preferred)

Work Location: In person