Principal AI Engineering Lead

Role Summary We are looking for a hands-on Principal AI Engineering Lead to drive engineering transformation and guiding the adoption of defined and scalable AI engineering practices that improve delivery quality, security, maintainability, and team maturity across the Indonesian tech team, while leading the end-to-end engineering execution of automation workflows and mini apps within Aura, Acclime's AI-powered automation platform being deployed across 18 APAC markets. This is not a traditional management role. You will need to hands-on with the code (writing and reviewing), design solutions, lead a team of developers, translate business processes into working software and can see the big picture and thrives in the details. You will bridge the gap between process analysts who understand the 'what' and developers who build the 'how.' Key Responsibilities Hands-On Technical Leadership Hands-on with the code — leading by example, not from the sidelinesLeverage reusable AI engineering patterns, evaluation frameworks, model integration standards, and observability practices for production-grade AI solutions.Establish coding standards, review processes, and technical best practices across the teamEmbed secure-by-design and responsible AI practices across solution design, data handling, model usage, deployment, and ongoing operations. Solution Design & Process Mapping Translate business process maps into technical specifications and system designsWork with process analysts to understand each mini-app workflow's business logic and edge casesCreate technical documentation, decision records, and architecture diagramsMap dependencies between workflows and identify reusable components Team Leadership & Delivery Drive team transformation by improving engineering operating models, delivery discipline, accountability, and adoption of new development standards across the Indonesia-based technology team.Coach engineers and technical leads through organisational and technical change, developing a culture of ownership, continuous improvement, and engineering excellence.Partner closely with DevSecOps, QA, Product, and Project Management functions to improve release governance, engineering hygiene, dependency management, and delivery predictability. Stakeholder & Business Translation Present technical progress, risks, and trade-offs to non-technical stakeholdersParticipate in programme committee meetings with clear, outcome-focused updatesConvert business requirements into achievable technical roadmapsSupport change management by explaining technical capabilities and limitations to country teams Required Experience & Skills Must Have 8+ years software engineering experience with at least 3 years in a technical lead or engineering manager role where you continued codingProven experience leading enterprise-scale AI engineering initiatives, including AI solution design, experimentation, evaluation, integration, deployment, and supportStrong full-stack development skills — comfortable across backend, APIs, and front-end as neededStrong engineering governance experience covering secure SDLC, code review practices, CI/CD, automated testing, environment management, release readiness, and operational supportWorking knowledge of AI/ML integration — prompt engineering, API orchestration, document AI/OCR, embedding modelsExperience with cloud platforms (Azure and GCP preferred)Track record of translating business processes into technical solutions in a services or enterprise contextDemonstrated ability to lead and grow development teams (4-10 people)Strong communication skills — able to explain technical concepts to business stakeholders Highly Desirable Experience in corporate services, professional services, or regulated industries across multiple marketsTeam transformational leadership experience is highly desirable, including engineering maturity uplift, operating model transformation, coaching, organisational change management, and adoption of new ways of working across distributed teamsExperience implementing secure SDLC practices, application security controls, vulnerability management, cloud security governance, and DevSecOps operating modelsExperience with private equity-backed growth environments and delivery pace expectationsUnderstanding of APAC business environments and multi-market deployment challenges Tech Stack You'll Work With Automation: workflow orchestration platforms, custom integrationsAI/ML: Azure AI Foundry, OpenAI, Anthropic Claude, Google Gemini — multi-model approachCloud: Microsoft Azure (primary)Data: Document AI, OCR pipelines, structured data extractionTools: Git, CI/CD, project management tooling What Success Looks Like First 60 Days Complete an engineering maturity assessment covering software delivery, DevSecOps practices, AI governance, release management, and team operating model effectivenessEstablish a transformation roadmap to guide adoption of newly developed engineering standards, quality assurance, security controls, and AI engineering governanceDeep understanding of all active automation mini-app workflows and their business contextEstablished working relationships with process analysts, project managers, and country stakeholdersShipping code and leading the team through at least one full sprint cycle First 6 Months New development structure consistently adopted by the Indonesia-based engineering team with measurable improvements in delivery quality, engineering discipline, and release governanceProduction deployment of multiple automation workflows across target marketsAI engineering standards established and adopted across Aura and future AI-enabled productsMeasurable improvement in team velocity and code quality