Applied AI Engineer

K2 Strategic (K2 Data Centres)

Singapore, SingaporeMidFull-timeQA/QC Sign in for match

About the role

Job Summary

The Applied AI Engineer supports POSH's AI programme by contributing to the delivery of prioritised use cases — assisting in the development of AI agents and solutions built on the Group's enterprise AI platform. This person will develop hands-on experience across the full AI delivery lifecycle: from translating business requirements into working prototypes, to deployment and user adoption support.

This is not a purely technical role. Equal weight is placed on business engagement, user adoption, and solution delivery as on technical execution. Prior domain knowledge is not a requirement for this role. This person is expected to spend time with the various functional and operational teams in POSH — understanding how the business works, what problems matter, and what good looks like in practice — and to bring that understanding back into everything they build.

Key Responsibilities

Use Case Delivery & Business Engagement

Work with POSH business functions to understand operational workflows, identify AI opportunities, and validate that proposed solutions address the right problems.
Translate business requirements into technical specifications and working prototypes.
Facilitate structured user acceptance testing sessions with operational teams. Presenting solutions, capturing feedback, and documenting gaps for iteration
Manage expectations proactively on timelines, data dependencies, and scope.
Develop HITL review interfaces and feedback mechanisms in close collaboration with domain experts.
Support the handover of deployed solutions to operational owners with documentation, user guides, and structured walkthroughs.

Data & System Integration

Assess data quality, validate schemas, and surface integration issues with the Business Excellence Team and data owners.
Integrate AI solutions with POSH's / Group’s enterprise systems using the integration standards and infrastructure.
Flag aspirational data assumptions and system-dependent dependencies early for programme-level decision

AI Agent Development

Configure and deploy AI agents on the enterprise platform — applying agentic design patterns including multi-agent architectures, tool-use, and autonomous workflow execution.
Build and maintain RAG pipelines for POSH-specific knowledge bases: ingestion, chunking, embedding, vector indexing, and retrieval optimisation.
Implement LLM integration — prompt engineering, structured output design, and iterative refinement for operational and regulatory quality standards
Build document processing components: OCR pipeline integration, multimodal content extraction and pre-processing for AI consumption.
Document and escalate platform gaps or POSH-specific requirements for COE review.

Testing, Quality Assurance & Continuous Improvement

Assist in developing and maintaining AI evaluation frameworks for POSH use cases: accuracy benchmarking, hallucination detection, retrieval quality scoring, and regression testing across model updates.
Conduct structured testing of agent behaviour before any production deployment, including edge cases, adversarial inputs, and failure mode analysis
Monitor deployed solutions; escalate anomalies promptly and support resolution with clear documentation.
Collaborate with the Group Data & Automation COE on platform standards and technical governance.
Document all agents, configurations, data schemas, and integration points to enterprise standards.

Key Requirements

Experience

1-2 years of software engineering or data/AI engineering experience – including exposure to LLM-based development in any context. Internships, research attachments, and early-career roles are all considered.
Hands-on experience with at least one AI or ML project from concept to working output. Production deployment is advantageous but not required
Some experience, or a demonstrated interest in, working with non-technical users: explaining technical concepts in plain language, gathering requirements, or presenting findings.
Final-year graduates with strong AI project portfolios are encouraged to apply.

Essential Technical Skills

Proficiency in Python for AI/ML pipeline development and data processing.
Working knowledge of LLM-based application development: RAG pipelines, prompt engineering, and structured output handling.
Familiarity with agentic AI design patterns and orchestration frameworks – multi-agent architectures, tool-use, and human-in-the-loop workflows.
Exposure to cloud-native development (Microsoft Azure preferred) and vector databases
Good understanding of document processing pipelines — OCR, PDF/image extraction, chunking strategies, and multimodal content handling.
Basic understanding of API design (REST/ async) and microservices integration.

Advantageous — Domain & Industry Knowledge

Familiarity with maritime, offshore, or similarly regulated, asset-heavy industries.
Exposure to compliance-driven environments where audit trails and regulatory alignment are operational necessities.
Familiarity with SharePoint / Microsoft 365, SAP, or equivalent enterprise platforms.

Personal Attributes

Curiosity: Genuinely interested in how operations work and how AI can improve them — not just in the technology itself.
Structured thinking: Manages concurrent tasks, communicates priorities clearly, and keeps stakeholders informed.
Credibility with users: Communicates clearly without jargon. Earns trust through listening and delivering solutions that fit how people actually work.
Ownership mindset: Takes initiative, follows through, raises blockers early — and knows when to escalate rather than proceed alone.
Comfort with ambiguity: Makes sound technical decisions when requirements are evolving. Moves forward pragmatically and documents assumptions.
Engineering rigour: Builds things others can understand, maintain, and build on. Takes documentation and testing seriously.