Gravitas Recruitment Group
1 week ago
AI Engineer - Gravitas Recruitment Group
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Company information
- Company
- Gravitas Recruitment Group
- Location
- Singapore Singapore
- Posted
- 1 week ago
Job description
AI Platform Engineer
The AI Platform Engineer supports the implementation and lifecycle management of assigned agent services on Central AI Kitchen. The role contributes to secure, governed connectivity between AI agents, enterprise tools, APIs and data services, while working across AI, infrastructure, cybersecurity, architecture and application teams.
Role Overview
The role supports the Lead AI Platform Engineer and development teams in delivering trusted agent identities, secure interactions and enterprise agent registry capabilities. It covers implementation, testing, troubleshooting, deployment and operational support for assigned platform services, working with internal staff, offshore engineers, vendors and other platform teams.
Key Skills & Experience
Bachelor’s degree in Computer Science, Engineering or a related field
2–4 years of experience in software engineering, platform engineering, cloud-native infrastructure, DevOps or data and AI platform roles
Hands-on experience with Red Hat OpenShift, Kubernetes or equivalent container platforms
Experience developing, deploying or integrating APIs, microservices or distributed application services
Experience with software development, scripting, system integration or platform automation
Experience working in engineering teams using source control, CI/CD and structured software delivery practices
Understanding of REST APIs, API gateways, service-to-service communication and distributed system integration
Basic understanding of enterprise identity and security concepts, including OAuth2, OIDC, RBAC, service identities and secrets management
Basic understanding of GenAI and AI agent architectures, including agents, tools, models and APIs
Experience developing or supporting containerised microservices
Familiarity with Git, GitOps, CI/CD and Infrastructure-as-Code practices
Understanding of observability concepts, including logging, metrics, tracing and operational monitoring
Working knowledge of cybersecurity principles, including least privilege, credential management, authentication and authorisation
Good analytical and problem-solving skills
Ability to learn new technologies and apply them to practical engineering problems
Good communication skills and the ability to work effectively within engineering teams
Ability to collaborate across AI, infrastructure, cybersecurity, architecture and application teams
Ability to work independently on assigned tasks with guidance from senior engineers
Willingness to document work, share knowledge and follow established engineering practices
Key Responsibilities
Support implementation and lifecycle management of assigned agent services on Central AI Kitchen
Implement and operate MCP servers and MCP gateway capabilities
Support implementation of agent identity and authentication capabilities
Implement and operate components of the enterprise agent registry
Implement security controls for authentication, authorisation, secrets management, tool access and least-privilege execution by AI agents
Perform testing, troubleshooting, defect resolution, deployment and operational support for assigned platform services
Maintain technical documentation, deployment configurations, test cases, operating procedures and knowledge articles for assigned components
Work with internal staff, offshore engineers, vendors and other platform teams to support delivery and operation of assigned capabilities
Requirements
On-site working arrangement
If you have the relevant skills and experience, please apply with an updated CV.
Required skills
- engineering
- documentation
- ci/cd
- communication skills
- analytical skills
- software development
- ai
- kubernetes
- troubleshooting
- software engineering
- independent work
- git
- collaboration
- testing
- tools
- apis
- devops
- genai
- problem-solving skills
- infrastructure
- architecture
- rest apis
- microservices
- computer science
- ai agents
- cybersecurity
- scripting
- oidc
- oauth2
- implementation
- gitops
- knowledge sharing
- technical documentation
- models
- system integration
- deployment
- development teams
- authentication
- operational support
- test cases
- cv
- logging
- metrics
- infrastructure-as-code
- platform engineering
- vendors
- container platforms
- enterprise tools
- rbac
- defect resolution
- agents
- security concepts
- red hat openshift
- security controls
- tracing
- source control
- authorisation
- platform automation
- secrets management
- operating procedures
- data services
- cloud-native infrastructure
- engineering practices
- application teams
- internal staff
- api gateways
- knowledge articles
- mcp servers
- cybersecurity principles
- least privilege
- platform teams
- platform services
- operational monitoring
- tool access
- ai platform engineer
- ai agent architectures
- credential management
- agent identity
- containerised microservices
- service-to-service communication
- deployment configurations
- enterprise identity
- data and ai platform
- mcp gateway
- central ai kitchen
- lead ai platform engineer
- agent identities
- secure interactions
- enterprise agent registry
- offshore engineers
- distributed application services
- structured software delivery
- distributed system integration
- service identities
- observability concepts
- agent services
- authentication capabilities
- enterprise agent registry components
- least-privilege execution
- on-site working arrangement
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