{"id":3731916,"url":"https://alion.io/job/fpt-software-m01-senior-ai-platform-engineer","title":"M01 - Senior AI Platform Engineer","company":{"id":49426,"name":"FPT Software","domain":"fptsoftware.com","url":"https://alion.io/company/fpt-software","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Join","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":6500,"max":8500,"currency":"SGD","period":"month","gross":true,"usd_annual":79620},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Angular","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Confluence","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GitLab","optional":false},{"name":"IAM","optional":false},{"name":"Incident 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API","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Terraform","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-11-04T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-11-04T00:00:00Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"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.\nThe 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.\nYou 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.\nKey Responsibilities\nDesign, develop, and maintain a centralised AI Foundation Platform using Amazon Bedrock to support AI-powered applications and services.\nBuild and manage secure AI infrastructure, including model access, provisioning, quotas, API integrations, and service configurations.\nDevelop full-stack platform services, including backend APIs, middleware, administrative dashboards, and self-service portals.\nIntegrate AI capabilities with enterprise applications and data sources to enable intelligent automation and contextual AI responses.\nDesign and implement Retrieval-Augmented Generation (RAG) pipelines, knowledge bases, embeddings, and vector databases.\nSupport model customisation, prompt management, fine-tuning, and domain-specific AI knowledge development.\nDevelop reusable AI platform components, APIs, connectors, and shared services for enterprise-wide adoption.\nOptimise AI platform performance, latency, throughput, scalability, availability, and operational costs.\nMonitor Amazon Bedrock consumption and recommend cost optimisation strategies, including model selection, prompt caching, and provisioned throughput.\nImplement secure AI infrastructure using Identity and Access Management (IAM), network isolation, encryption, and secrets management.\nDevelop Infrastructure-as-Code (IaC) solutions and CI/CD pipelines to automate infrastructure provisioning, deployment, and release management.\nDeploy and maintain monitoring and observability tools to track AI platform performance, service utilisation, and system health.\nTroubleshoot infrastructure and application issues, perform root cause analysis, and support incident management.\nDesign and implement agentic AI infrastructure using technologies such as Amazon Bedrock Agents, AgentCore, and other AI orchestration frameworks.\nDevelop reusable AI agents, tools, connectors, and orchestration patterns, including multi-agent workflows.\nImplement agent identity management, least-privilege access controls, secure tool execution, and approved agent registries.\nConfigure agent usage limits, including token consumption, API rates, execution steps, timeouts, and spending thresholds.\nImplement agent observability using distributed tracing, logging, metrics, and alerting to monitor model calls, tool execution, and agent decisions.\nSupport the integration of agentic AI capabilities into enterprise applications and business workflows.\nCollaborate with solution architects, DevSecOps engineers, and application development teams to deliver end-to-end AI solutions.\nConduct code reviews, architecture reviews, and technical assessments to ensure engineering quality.\nPrepare technical documentation, architecture designs, operational runbooks, and onboarding guides.\nSupport security reviews, architecture submissions, and compliance with relevant ICT security requirements, including IM8 and System Security Plan (SSP).\nRequirements\nDiploma or Degree in Computer Science, Computer Engineering, Information Technology, or a related discipline.\nMinimum 5 years of relevant experience in full-stack development, cloud engineering, or AI platform engineering.\nAt least 2 years of practical experience working with AI/ML or Generative AI technologies.\nHands-on experience in at least 2 full software development lifecycles.\nStrong hands-on experience with AWS services, particularly Amazon Bedrock, SageMaker, Lambda, API Gateway, S3, OpenSearch, IAM, VPC, CloudWatch, and KMS.\nStrong programming skills in Python and JavaScript/TypeScript.\nExperience developing RESTful APIs using frameworks such as FastAPI or Node.js.\nExperience developing web applications using React, Angular, or similar frontend frameworks.\nHands-on experience building RAG pipelines, integrating LLM APIs, and working with embeddings and vector databases.\nExperience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation.\nExperience implementing CI/CD pipelines and working with DevOps tools such as GitLab, Jira, Bitbucket, and Confluence.\nKnowledge of containerisation and orchestration technologies such as Docker and Kubernetes.\nStrong understanding of cloud security, identity and access management, encryption, and data protection controls.\nHands-on experience developing agentic AI systems, including tool calling, multi-agent orchestration, and agent memory.\nExperience with AI orchestration frameworks such as Amazon Bedrock Agents, LangGraph, or Strands Agents.\nFamiliarity with integrating AI agents and tools through APIs or Model Context Protocol (MCP).\nUnderstanding of AI observability, distributed tracing, SLIs/SLOs, and monitoring frameworks.\nKnowledge of government ICT security requirements, including IM8 and SSP, and their application to AI platform security and governance.\nStrong troubleshooting, analytical, and problem-solving skills.\nComfortable working in Agile development environments.\nExcellent communication, documentation, and stakeholder management skills.\nAdded Advantages\nExperience with Government Commercial Cloud (GCC) and government ICT security requirements.\nAWS certifications such as Solutions Architect, Machine Learning Specialty, or Generative AI Developer.\nExperience with LLM orchestration frameworks such as LangChain, LlamaIndex, or Amazon Bedrock Agents.\nExperience implementing AI solutions in the public sector, legal industry, or other regulated environments.\nSkills\nRetrieval-Augmented Generation (RAG), Cloudwatch, Amazon Bedrock, Cloudformation, Intelligent Automation, Throughput, Computer Science, API Gateway, Angular (Web Framework), AI Safety, portal services, Software Development Lifecycle, Develop Software Components, Rest Apis, Knowledge Engineering, 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