AI Architect & Engineering Consultant

AVN SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 130,000 - 210,000; Full time

Job summary

AVN Solutions Pte. Ltd. is seeking an AI Architect & Engineering Consultant to design scalable AI architectures and lead technical assessments across edge, cloud, and on‑premises environments.

You will define governance frameworks, reference architectures, and integration patterns, collaborating with enterprise teams to drive engineering standards and reusable components that accelerate AI delivery.

Responsibilities

• Pre-Project Technical Consulting
• Conduct comprehensive technical assessments to estimate the effort, complexity, and resource requirements for new AI products and features.
• Provide expert guidance on the feasibility, scalability, risks, and business impact of proposed AI solutions.
• Advise stakeholders on technology selection, architecture approaches, and implementation strategies.
• Design, establish, and maintain a scalable and secure AI architecture framework that supports deployment across edge, cloud, and on-premises environments.
• Define reference architectures, design patterns, and integration frameworks to ensure consistency across AI products and platforms.
• Ensure seamless interoperability, lifecycle management, and version control of AI models, services, and products.
• Define end-to-end processes for AI model development, including data preparation, training, validation, testing, deployment, monitoring, and operations within a microservices-based ecosystem.
• Establish and govern technical standards, architectural principles, and best practices to ensure quality, reliability, security, and maintainability.
• Develop governance frameworks covering AI engineering, model management, MLOps, observability, and AI service operations.
• Define and implement interaction patterns for AI microservices to ensure effective communication with enterprise applications and non-AI services.
• Design robust APIs, interfaces, and integration mechanisms that support scalable, secure, and high-performance service interactions.
• Ensure interoperability across platforms, systems, and technology domains.
• Identify opportunities to leverage common platforms, reusable components, and shared capabilities across AI products to improve efficiency and reduce duplication.
• Define, implement, and enforce coding standards, development practices, and quality controls across AI engineering teams.
• Drive continuous improvement of architecture, development methodologies, and operational processes.
• Develop platforms, tools, templates, and frameworks that enable teams to independently develop, deploy, and manage AI solutions.
• Promote engineering autonomy through reusable assets, automation, and self-service capabilities.
• Continuously evaluate emerging technologies, industry trends, and best practices to advance the organisation's AI capabilities and innovation agenda.
• Recommend and drive adoption of new technologies that create strategic business value.
• Partner closely with shared services teams, including infrastructure, cybersecurity, data, and enterprise architecture functions, to ensure alignment, governance, and operational support for AI initiatives.
• Act as the primary architectural authority for AI-related programmes and strategic technology decisions.
• Provide technical leadership and governance across internal and external development teams delivering AI solutions.
• Resolve technical dependencies, architectural challenges, and delivery impediments to ensure successful project execution.
• Ensure adherence to established architectural standards, engineering principles, and development best practices.
• Partner with Product Owners and business stakeholders to translate business requirements into scalable technical solutions.
• Provide technical direction and engineering expertise throughout the product lifecycle.
• Define technical enablers and architecture runway requirements to support product roadmap delivery.
• Define and implement appropriate testing strategies, including functional, performance, security, integration, and model validation testing.
• Ensure AI solutions meet required standards for reliability, scalability, accuracy, and operational performance.
• Contribute to technical planning, release management, and deployment activities while ensuring alignment with architectural principles.
• Act as a trusted technical adviser to AI Architects and delivery teams, providing expertise on architecture decisions, implementation challenges, and technology choices.
• Evaluate technical requirements, identify risks, and recommend mitigation strategies to support successful delivery

Job description

Join Our Team as an AI Architect & Engineering Consultant!

About Us:

We are at the forefront of AI innovation, dedicated to developing cutting-edge solutions that push the boundaries of technology. Our team is driven by a passion for excellence and a commitment to delivering impactful AI solutions.

Position:

AI Architect & Engineering Consultant

Tasks & Responsibilities:

A. AI Architecture Services:

Pre-Project Technical Consulting

Conduct comprehensive technical assessments to estimate the effort, complexity, and resource requirements for new AI products and features.

Provide expert guidance on the feasibility, scalability, risks, and business impact of proposed AI solutions.

Advise stakeholders on technology selection, architecture approaches, and implementation strategies.

AI Architecture Development

Design, establish, and maintain a scalable and secure AI architecture framework that supports deployment across edge, cloud, and on-premises environments.

Define reference architectures, design patterns, and integration frameworks to ensure consistency across AI products and platforms.

Ensure seamless interoperability, lifecycle management, and version control of AI models, services, and products.

Framework and Standards Definition

Define end-to-end processes for AI model development, including data preparation, training, validation, testing, deployment, monitoring, and operations within a microservices-based ecosystem.

Establish and govern technical standards, architectural principles, and best practices to ensure quality, reliability, security, and maintainability.

Develop governance frameworks covering AI engineering, model management, MLOps, observability, and AI service operations.

Service Integration and Interoperability

Define and implement interaction patterns for AI microservices to ensure effective communication with enterprise applications and non-AI services.

Design robust APIs, interfaces, and integration mechanisms that support scalable, secure, and high-performance service interactions.

Ensure interoperability across platforms, systems, and technology domains.

Operational Excellence and Engineering Standards

Identify opportunities to leverage common platforms, reusable components, and shared capabilities across AI products to improve efficiency and reduce duplication.

Define, implement, and enforce coding standards, development practices, and quality controls across AI engineering teams.

Drive continuous improvement of architecture, development methodologies, and operational processes.

Self-Service Enablement

Develop platforms, tools, templates, and frameworks that enable teams to independently develop, deploy, and manage AI solutions.

Promote engineering autonomy through reusable assets, automation, and self-service capabilities.

Technology Strategy and Innovation

Continuously evaluate emerging technologies, industry trends, and best practices to advance the organisation's AI capabilities and innovation agenda.

Recommend and drive adoption of new technologies that create strategic business value.

Cross-Functional Collaboration

Partner closely with shared services teams, including infrastructure, cybersecurity, data, and enterprise architecture functions, to ensure alignment, governance, and operational support for AI initiatives.

Act as the primary architectural authority for AI-related programmes and strategic technology decisions.

B. AI Engineering Services

Development Governance

Provide technical leadership and governance across internal and external development teams delivering AI solutions.

Resolve technical dependencies, architectural challenges, and delivery impediments to ensure successful project execution.

Ensure adherence to established architectural standards, engineering principles, and development best practices.

Product and Technical Enablement

Partner with Product Owners and business stakeholders to translate business requirements into scalable technical solutions.

Provide technical direction and engineering expertise throughout the product lifecycle.

Define technical enablers and architecture runway requirements to support product roadmap delivery.

Testing, Quality Assurance and Release Planning

Define and implement appropriate testing strategies, including functional, performance, security, integration, and model validation testing.

Ensure AI solutions meet required standards for reliability, scalability, accuracy, and operational performance.

Contribute to technical planning, release management, and deployment activities while ensuring alignment with architectural principles.

Technical Consulting and Architecture Support

Act as a trusted technical adviser to AI Architects and delivery teams, providing expertise on architecture decisions, implementation challenges, and technology choices.

Evaluate technical requirements, identify risks, and recommend mitigation strategies to support successful delivery