AI Engineering

Key Responsibilities
End-to-End Solution Design

• Own the technical solution design for all AI use cases across JJW's ERP implementation lifecycle —requirements gathering, configuration, data migration, testing, and go-live support — from intake through to production, producing architecture artefacts, design decisions and technical specifications.

• Work alongside JJW's Enterprise and Cloud Architects on end-to-end solution design — ensuring AI solutions are enterprise-compliant, secure and platform-aligned from day one across client engagements in Vietnam, Singapore, and the US.

• Produce and review architecture artefacts, technical designs, integration patterns, engineering standards, andimplementation plans.

AI Platform & Services —Design & Oversight

• Own the AI engineering layer onAWS — defining which native services are used for each JJW use case (e.g.,legacy data extraction for ERP migration, automated test case generation, client requirement analysis, ERP knowledge base support) and how they connector the enterprise stack.

• Lead technical design across the core AWS AI stack: Bedrock (LLM orchestration, agents, knowledge bases,guardrails), Agent Core, Sage Maker (ML model deployment, pipelines, MLOps),Textract/Comprehend (document intelligence for legacy records and client source documents during ERP migrations), OpenSearch (vector search, RAG retrieval over ERP configuration and implementation documentation), Step Functions (pipeline orchestration).

Production Engineering &Standards

• Ensure all AI solutions are built to production standards — with appropriate testing, security controls,PII handling, monitoring, logging, resilience, error handling and operational runbooks.

• Own the AI Platform Engineering reuse library — ensuring components, patterns, prompt templates and AWS service wrappers built for one client engagement are packaged and available for reuse across other ERP implementation projects.

• Lead technical production readiness reviews before any AI solution goes live — covering model risk,security, performance, cost and rollback plan.

Team Leadership

• Lead the AI Platform Engineering team across AI engineering, AI solution development, and AI testing disciplines.

• Provide technical direction,coaching, and development support to AI Engineers, AI Engineer Associates, andAI Test Engineers.

What We're Looking For
Essential

• 7+ years of software or AI engineering experience, with at least 3 years in a senior technical lead or architect role.

• Strong hands-on experience designing and delivering AI / ML / GenAI solutions in production environments.

• Deep understanding of AWS AI and cloud-native services, preferably including Bedrock, Sage Maker, Textract,Comprehend, OpenSearch, Lambda, API Gateway, Step Functions, and S3.

• Strong practical knowledge of Generative AI engineering, including LLM integration, prompt engineering, RAG architecture, embeddings, vector stores, evaluation frameworks, and hallucination controls.

• Exposure to ERP implementation projects (e.g., SAP, Oracle, Microsoft Dynamics) and multi-market clientdelivery across Vietnam, Singapore, and the US is a plus.