Description review
M01 - Senior AI Platform Engineer
FPT Asia Pacific Pte Ltd · Singapore · back to the listing
HR standards
60/100
needs work
Title ↔ description
89/100
strong
Reads as
Full-Stack Engineer
85% confident
How others title the same work
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What the listing never says
- 48 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Role Overview
We are looking for an experienced Senior AI Platform Engineer (Full Stack) to design, develop, and maintain a centralised AI Foundation Platform that enables secure, scalable, and reliable AI capabilities across enterprise applications.
The successful candidate will be responsible for developing AI platform services, integrating large language models (LLMs), implementing Retrieval-Augmented Generation (RAG) solutions, and building reusable AI components.
You will also play a key role in developing agentic AI infrastructure, managing cloud-based AI services, and ensuring that AI solutions meet enterprise security, performance, and governance requirements.
Key Responsibilities
• Design, develop, and maintain a centralised AI Foundation Platform using Amazon Bedrock to support AI-powered applications and services.
• Build and manage secure AI infrastructure, including model access, provisioning, quotas, API integrations, and service configurations.
• Develop full-stack platform services, including backend APIs, middleware, administrative dashboards, and self-service portals.
• Integrate AI capabilities with enterprise applications and data sources to enable intelligent automation and contextual AI responses.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, knowledge bases, embeddings, and vector databases.
• Support model customisation, prompt management, fine-tuning, and domain-specific AI knowledge development.
• Develop reusable AI platform components, APIs, connectors, and shared services for enterprise-wide adoption.
• Optimise AI platform performance, latency, throughput, scalability, availability, and operational costs.
• Monitor Amazon Bedrock consumption and recommend cost optimisation strategies, including model selection, prompt caching, and provisioned throughput.
• Implement secure AI infrastructure using Identity and Access Management (IAM), network isolation, encryption, and secrets management.
• Develop Infrastructure-as-Code (IaC) solutions and CI/CD pipelines to automate infrastructure provisioning, deployment, and release management.
• Deploy and maintain monitoring and observability tools to track AI platform performance, service utilisation, and system health.
• Troubleshoot infrastructure and application issues, perform root cause analysis, and support incident management.
• Design and implement agentic AI infrastructure using technologies such as Amazon Bedrock Agents, AgentCore, and other AI orchestration frameworks.
• Develop reusable AI agents, tools, connectors, and orchestration patterns, including multi-agent workflows.
• Implement agent identity management, least-privilege access controls, secure tool execution, and approved agent registries.
• Configure agent usage limits, including token consumption, API rates, execution steps, timeouts, and spending thresholds.
• Implement agent observability using distributed tracing, logging, metrics, and alerting to monitor model calls, tool execution, and agent decisions.
• Support the integration of agentic AI capabilities into enterprise applications and business workflows.
• Collaborate with solution architects, DevSecOps engineers, and application development teams to deliver end-to-end AI solutions.
• Conduct code reviews, architecture reviews, and technical assessments to ensure engineering quality.
• Prepare technical documentation, architecture designs, operational runbooks, and onboarding guides.
• Support security reviews, architecture submissions, and compliance with relevant ICT security requirements, including IM8 and System Security Plan (SSP).
Requirements
• Diploma or Degree in Computer Science, Computer Engineering, Information Technology, or a related discipline.
• Minimum 5 years of relevant experience in full-stack development, cloud engineering, or AI platform engineering.
• At least 2 years of practical experience working with AI/ML or Generative AI technologies.
• Hands-on experience in at least 2 full software development lifecycles.
• Strong hands-on experience with AWS services, particularly Amazon Bedrock, SageMaker, Lambda, API Gateway, S3, OpenSearch, IAM, VPC, CloudWatch, and KMS.
• Strong programming skills in Python and JavaScript/TypeScript.
• Experience developing RESTful APIs using frameworks such as FastAPI or Node.js.
• Experience developing web applications using React, Angular, or similar frontend frameworks.
• Hands-on experience building RAG pipelines, integrating LLM APIs, and working with embeddings and vector databases.
• Experience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation.
• Experience implementing CI/CD pipelines and working with DevOps tools such as GitLab, Jira, Bitbucket, and Confluence.
• Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of cloud security, identity and access management, encryption, and data protection controls.
• Hands-on experience developing agentic AI systems, including tool calling, multi-agent orchestration, and agent memory.
• Experience with AI orchestration frameworks such as Amazon Bedrock Agents, LangGraph, or Strands Agents.
• Familiarity with integrating AI agents and tools through APIs or Model Context Protocol (MCP).
• Understanding of AI observability, distributed tracing, SLIs/SLOs, and monitoring frameworks.
• Knowledge of government ICT security requirements, including IM8 and SSP, and their application to AI platform security and governance.
• Strong troubleshooting, analytical, and problem-solving skills.
• Comfortable working in Agile development environments.
• Excellent communication, documentation, and stakeholder management skills.
Added Advantages
• Experience with Government Commercial Cloud (GCC) and government ICT security requirements.
• AWS certifications such as Solutions Architect, Machine Learning Specialty, or Generative AI Developer.
• Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or Amazon Bedrock Agents.
• Experience implementing AI solutions in the public sector, legal industry, or other regulated environments.
Originally posted on Himalayas
We are looking for an experienced Senior AI Platform Engineer (Full Stack) to design, develop, and maintain a centralised AI Foundation Platform that enables secure, scalable, and reliable AI capabilities across enterprise applications.
The successful candidate will be responsible for developing AI platform services, integrating large language models (LLMs), implementing Retrieval-Augmented Generation (RAG) solutions, and building reusable AI components.
You will also play a key role in developing agentic AI infrastructure, managing cloud-based AI services, and ensuring that AI solutions meet enterprise security, performance, and governance requirements.
Key Responsibilities
• Design, develop, and maintain a centralised AI Foundation Platform using Amazon Bedrock to support AI-powered applications and services.
• Build and manage secure AI infrastructure, including model access, provisioning, quotas, API integrations, and service configurations.
• Develop full-stack platform services, including backend APIs, middleware, administrative dashboards, and self-service portals.
• Integrate AI capabilities with enterprise applications and data sources to enable intelligent automation and contextual AI responses.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, knowledge bases, embeddings, and vector databases.
• Support model customisation, prompt management, fine-tuning, and domain-specific AI knowledge development.
• Develop reusable AI platform components, APIs, connectors, and shared services for enterprise-wide adoption.
• Optimise AI platform performance, latency, throughput, scalability, availability, and operational costs.
• Monitor Amazon Bedrock consumption and recommend cost optimisation strategies, including model selection, prompt caching, and provisioned throughput.
• Implement secure AI infrastructure using Identity and Access Management (IAM), network isolation, encryption, and secrets management.
• Develop Infrastructure-as-Code (IaC) solutions and CI/CD pipelines to automate infrastructure provisioning, deployment, and release management.
• Deploy and maintain monitoring and observability tools to track AI platform performance, service utilisation, and system health.
• Troubleshoot infrastructure and application issues, perform root cause analysis, and support incident management.
• Design and implement agentic AI infrastructure using technologies such as Amazon Bedrock Agents, AgentCore, and other AI orchestration frameworks.
• Develop reusable AI agents, tools, connectors, and orchestration patterns, including multi-agent workflows.
• Implement agent identity management, least-privilege access controls, secure tool execution, and approved agent registries.
• Configure agent usage limits, including token consumption, API rates, execution steps, timeouts, and spending thresholds.
• Implement agent observability using distributed tracing, logging, metrics, and alerting to monitor model calls, tool execution, and agent decisions.
• Support the integration of agentic AI capabilities into enterprise applications and business workflows.
• Collaborate with solution architects, DevSecOps engineers, and application development teams to deliver end-to-end AI solutions.
• Conduct code reviews, architecture reviews, and technical assessments to ensure engineering quality.
• Prepare technical documentation, architecture designs, operational runbooks, and onboarding guides.
• Support security reviews, architecture submissions, and compliance with relevant ICT security requirements, including IM8 and System Security Plan (SSP).
Requirements
• Diploma or Degree in Computer Science, Computer Engineering, Information Technology, or a related discipline.
• Minimum 5 years of relevant experience in full-stack development, cloud engineering, or AI platform engineering.
• At least 2 years of practical experience working with AI/ML or Generative AI technologies.
• Hands-on experience in at least 2 full software development lifecycles.
• Strong hands-on experience with AWS services, particularly Amazon Bedrock, SageMaker, Lambda, API Gateway, S3, OpenSearch, IAM, VPC, CloudWatch, and KMS.
• Strong programming skills in Python and JavaScript/TypeScript.
• Experience developing RESTful APIs using frameworks such as FastAPI or Node.js.
• Experience developing web applications using React, Angular, or similar frontend frameworks.
• Hands-on experience building RAG pipelines, integrating LLM APIs, and working with embeddings and vector databases.
• Experience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation.
• Experience implementing CI/CD pipelines and working with DevOps tools such as GitLab, Jira, Bitbucket, and Confluence.
• Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of cloud security, identity and access management, encryption, and data protection controls.
• Hands-on experience developing agentic AI systems, including tool calling, multi-agent orchestration, and agent memory.
• Experience with AI orchestration frameworks such as Amazon Bedrock Agents, LangGraph, or Strands Agents.
• Familiarity with integrating AI agents and tools through APIs or Model Context Protocol (MCP).
• Understanding of AI observability, distributed tracing, SLIs/SLOs, and monitoring frameworks.
• Knowledge of government ICT security requirements, including IM8 and SSP, and their application to AI platform security and governance.
• Strong troubleshooting, analytical, and problem-solving skills.
• Comfortable working in Agile development environments.
• Excellent communication, documentation, and stakeholder management skills.
Added Advantages
• Experience with Government Commercial Cloud (GCC) and government ICT security requirements.
• AWS certifications such as Solutions Architect, Machine Learning Specialty, or Generative AI Developer.
• Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or Amazon Bedrock Agents.
• Experience implementing AI solutions in the public sector, legal industry, or other regulated environments.
Originally posted on Himalayas