Gravitas Recruitment Group
1 hour from now
Architecture Lead - Gravitas Recruitment Group
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Company information
- Company
- Gravitas Recruitment Group
- Location
- Singapore Singapore
- Posted
- 1 hour from now
Job description
Lead AI Solution Architect
The Lead AI Solution Architect aligns enterprise AI solutions with architectural, security and governance requirements, enabling scalable delivery from prototype through operational support. The role supports the responsible adoption of emerging AI technologies within the company’s enterprise technology environment.
Role Overview
This role coordinates end-to-end AI solution integration across data sources, enterprise platforms, legacy systems and business applications. It provides technical alignment across business stakeholders, Product Owners, AI engineers, software developers, data teams, platform teams, IT and network domains.
Key Skills & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data, AI/ML or a related field
- 5 to 10 years of experience in solution architecture or technical leadership roles, with a strong focus on AI platform integration and API-driven systems
- Proven expertise in designing, reviewing and approving end-to-end AI workflows across hybrid cloud environments
- Experience leading and managing development teams focused on software engineering and AI solution delivery
- Strong background in AI/ML systems, cloud-native architectures and API ecosystems
- Hands-on experience with cloud-based AI services, including model deployment, monitoring and scaling
- Experience with AI/ML orchestration tools and frameworks, including retrieval-augmented generation and model lifecycle management technologies
- Strong technical acumen and understanding of software architecture, development frameworks and best practices
- Proficiency in API architecture and integration standards, including REST, GraphQL and gRPC
- Strong understanding of containerisation, virtual machines, microservices and MLOps tools
- Experience evaluating and integrating new AI technologies, frameworks and vendor solutions into enterprise environments
- Ability to communicate complex AI concepts clearly to executive, technical and non-technical audiences
- Experience managing stakeholders across business, IT and network domains
- Experience collaborating across cross-functional technical and non-technical teams
- Ability to adapt to evolving technologies and changing business priorities while maintaining focus on long-term enterprise value
- Strong technical writing and presentation skills
- Familiarity with the telecommunications domain, IT systems and data-driven use cases
- Keen interest in emerging AI and data technologies
Key Responsibilities
- Align value pool and project-level AI solutions with enterprise architecture principles, security requirements, governance standards and approved platform capabilities
- Design and coordinate end-to-end integration across data sources, enterprise platforms, legacy systems and business applications
- Apply and promote production-grade architecture practices across assigned projects
- Ensure solutions support scalability, reliability, observability, maintainability and operational readiness from prototype through deployment and Day 2 support
- Translate business requirements into clear and actionable solution designs
- Evaluate emerging AI technologies and architectural approaches for relevant value pool use cases
- Balance innovation, delivery speed, solution complexity, platform fit and long-term operational sustainability
Requirements
- On-site working pattern
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data, AI/ML or a related field
- 5 to 10 years of experience in solution architecture or technical leadership roles
If you have the relevant skills and experience, please apply with an updated CV.
Required skills
- grpc
- engineering
- it
- security
- monitoring
- reliability
- governance
- ai
- software engineering
- product owners
- machine learning
- solution architecture
- data
- technical leadership
- best practices
- presentation skills
- microservices
- observability
- cross-functional teams
- computer science
- stakeholder management
- technical writing
- ml
- graphql
- scalability
- frameworks
- rest
- software architecture
- emerging technologies
- deployment
- software developers
- development teams
- operational support
- data sources
- mlops
- cv
- cloud-based ai services
- ai workflows
- enterprise architecture
- operational readiness
- scaling
- on-site
- hybrid cloud
- maintainability
- executive communication
- containerisation
- business requirements
- prototype
- api architecture
- api-driven systems
- technical communication
- legacy systems
- ai solution architect
- solution designs
- virtual machines
- model deployment
- security requirements
- business applications
- retrieval-augmented generation
- ai/ml systems
- development frameworks
- platform capabilities
- technical acumen
- business stakeholders
- business priorities
- data teams
- enterprise platforms
- cloud-native architectures
- governance standards
- platform teams
- api ecosystems
- enterprise value
- model lifecycle management
- technical alignment
- integration standards
- ai engineers
- ai platform integration
- day 2 support
- vendor solutions
- solution integration
- non-technical communication
- delivery speed
- data-driven use cases
- evolving technologies
- operational sustainability
- network domains
- ai solution delivery
- ai/ml orchestration tools
- telecommunications domain
- value pool
- project-level ai solutions
- production-grade architecture
- solution complexity
- platform fit
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