AI Engineering Lead

ESPIRE INFOLABS (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 180,000 - 250,000; Full time

Job summary

ESPIRE INFOLABS (SINGAPORE) PTE. LTD. seeks an AI Engineering Lead to own the end-to-end engineering of AI solutions on AWS, from design with Enterprise and Cloud Architects through production delivery and governance.

This hands-on leadership role will guide solution design, review technical implementations, lead the engineering team, oversee vendor-delivered AI solutions, and establish production-grade discipline across the AI Platform Engineering function.

Qualifications

• Own the technical solution design for all AI use cases, from intake to production.
• Define standards for CI/CD, testing, observability and incident response.

Responsibilities

• Own the AI engineering layer on AWS and define native services for each use case.
• Lead design across core AWS AI stack: Bedrock, SageMaker, Textract/Comprehend, OpenSearch, Step Functions.
• Work with Cloud Platform Engineer to translate requirements into properly configured AWS environments.
• Own LLMOps and MLOps architecture including versioning, deployment, monitoring and retraining triggers.
• Oversee AI service cost architecture with FinOps tagging and cost allocation.
• Ensure production-grade testing, security controls, PII handling, monitoring and runbooks.
• Lead AI Platform Engineering team including engineers and testers; set coding standards and delivery discipline.
• Drive governance with AI Governance and Risk teams, producing architecture documents and risk mitigations.
• Establish and maintain AI engineering runbooks and operational standards across live AI solutions.

Skills

AI platform engineering

AWS

MLOps

LLMOps

GenAI

Security

Cost optimisation

Technical leadership

Solution design

Tools

Bedrock

SageMaker

Textract/Comprehend

OpenSearch

Step Functions

Job description

The AI Engineering Lead is the technical authority of the AI Platform Engineering. This role owns the end-to-end engineering of all AI solutions - from solution design alongside the Enterprise and Cloud Architects through to production delivery - and is accountable for the technical quality of everything builds or procures. The role is responsible for defining designs, builds, tests, deploys, governs, and operates AI solutions on AWS. This includes Generative AI, Agentic AI, Retrieval-Augmented Generation, document intelligence, LLM integration, AI workflow orchestration, MLOps / LLMOps, and reusable AI engineering standards. The AI Engineering Lead works closely with Enterprise Architects, Cloud Architects, Cloud Platform Engineers, Data Science & Analytics teams, AI Governance, and delivery teams to ensure AI solutions are secure, scalable, reusable, cost-aware, and aligned to enterprise technology standards. This is a hands-on leadership role. The AI Engineering Lead is expected to guide solution design, review technical implementation, lead the engineering team, oversee vendor-delivered AI solutions, and establish production-grade engineering discipline across the AI Platform Engineering function.

KEYRESPONSIBILITIES

End-to-End Solution Design

• Own the technical solution design for all AI use cases - from intake through to production - producing architecture artefacts, design decisions and technical specifications
• Work alongside the Enterprise and Cloud Architects on end-to-end solution design - ensuring AI solutions are enterprise-compliant, secure and platform-aligned from day one
• Produce and review architecture artefacts, technical designs, integration patterns, engineering standards and implementation plans.
• Provide technical input into build / buy / partner decisions by assessing feasibility, complexity, delivery risk, maintainability, cost, and long-term platform fit.
• Ensure AI solutions are designed for reuse, operational support, observability, governance and future extensibility.

AI Platform& Services - Design & Oversight

• Own the AI engineering layer on AWS - defining which native services are used for each use case and how they connect to 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), OpenSearch (vector search, RAG retrieval), Step Functions (pipeline orchestration)
• Work with the Cloud Platform Engineer - who owns environment provisioning and infrastructure setup - by providing clear technical requirements per use case so they can configure AWS environments correctly
• Own the LLMOps and MLOps architecture - model versioning, deployment, monitoring, drift detection, retraining triggers and production observability on AWS
• Oversee AWS AI service cost architecture - working with the AI Champion and Cloud Platform Engineer on FinOps tagging, per-use-case cost allocation and spend optimisation

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 are packaged and available for reuse
• Lead technical production readiness reviews before any AI solution goes live - covering model risk, security, performance, cost and rollback plan
• Define standards for CI/CD, automated testing, release management, observability, incident response and operational support for AI workloads.
• Establish and maintain AI engineering runbooks, support models, incident playbooks, and operational standards for live AI solutions.
• Work with AI Governance and Risk teams to provide required technical artefacts such as architecture documents, model cards, risk mitigations, control evidence, and production readiness sign-offs.
• Ensure governance requirements are embedded into delivery processes without creating unnecessary delivery friction.

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.
• Set engineering culture and delivery discipline, including code review standards, documentation expectations, test coverage, secure coding, reuse, and operational accountability.
• Run technical design reviews, engineering forums, sprint planning discussions, and implementation reviews in collaboration with delivery and product stakeholders.
• Support team capability building across AWS AI services, GenAI engineering, Agentic AI, LLMOps, MLOps, and production AI engineering.
• Build a stro