AI Engineering

Key Responsibilities

End-to-End Solution Design

• Own the technical solution design for all AI use cases across JSC's procurement, quality assurance, supplier management, and export operations — from intake through to production— producing architecture artefacts, design decisions and technical specifications.

• Work alongside JSC'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 Singapore, Vietnam, and India.

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

AI Platform & Services —Design & Oversight

• Own the AI engineering layer onAWS — defining which native services are used for each JSC use case (e.g.,RFQ/quote processing, supplier document intelligence, quality certificate verification, order book reconciliation) and how they connect to JSC's 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 invoices, purchase orders, qualitycertificates, and supplier quotes), OpenSearch (vector search, RAG retrieval over supplier and product 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 use case (e.g., a supplier document extractor) are packaged and available for reuse across JSC's product lines and regions.

• 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, and AI Test Engineers.

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 manufacturing, procurement, supply chain, or export/trading operations is a plus (fastener or industrial components industry preferred).