AI Cloud Solution Architect & Engineer

AI Cloud Solution Architect & Engineer Location: Jakarta, Indonesia

Job Type: Part‑time

Job Details

Seniority level: Mid‑Senior level

Employment type: Part‑time

Job function: Engineering and Information Technology

Industries: IT Services and IT Consulting

Responsibilities

Architecture & Design: Gather requirements, design cloud architectures, calculate ROI, and create technical proposals for AI/ML solutions.

Engineering Excellence: Build production‑grade infrastructure using IaC, develop APIs and prototypes, implement CI/CD pipelines, and manage AI workload operations.

Client Success: Transform business requirements into secure, scalable, cost‑effective solutions aligned with AWS best practices.

Knowledge Transfer: Create reusable artifacts, documentation, and architectural patterns to accelerate future project delivery.

Design solution architectures per month with comprehensive diagrams and specifications.

Achieve 80%+ client acceptance rate on proposed architectures and estimates.

Deliver ROI calculations and cost models within 2 business days of request.

Deploy infrastructure through IaC (AWS CDK/Terraform) with zero manual configuration.

Create at least 3 reusable IaC components or architectural patterns per quarter.

Implement CI/CD pipelines for all projects with automated testing and deployment.

Maintain 95%+ uptime for production AI/ML inference endpoints.

Ensure all solutions pass AWS Well‑Architected Review standards.

Deliver comprehensive documentation within 1 week of architecture completion.

Create simplified UIs/demos for PoC validation and client presentations.

Build and maintain cloud infrastructure using AWS CDK (primary) and Terraform.

Develop reusable IaC components and modules for common patterns.

Implement infrastructure for AI/ML workloads: GPU clusters, model serving, data lakes.

Develop Python applications: FastAPI backends, data processing scripts, automation tools.

Build and integrate RESTful APIs for AI model serving and data access.

Deploy and manage AI/ML model serving infrastructure (SageMaker endpoints, containerized models).

Deploy AI/ML pipelines: data ingestion, preprocessing, training automation, model deployment.

Automate deployment processes with infrastructure testing and validation.

Monitor, log, and alert using CloudWatch, Prometheus, Grafana.

Build data pipelines for AI training and inference using AWS Glue, Step Functions, Lambda.

Implement security best practices: IAM, VPC design, encryption, secrets management.

Ensure solutions meet regulatory requirements (PCI‑DSS, GDPR, SOC2, MAS TRM, etc.).

Optimize cloud spend for compute‑intensive AI workloads and GPU utilization.

Implement disaster recovery procedures for AI models and training data.

Troubleshoot and resolve production issues in AI infrastructure.

Provide technical guidance to project teams during implementation.

Skills

Strong solution architecture skills; translate business requirements into technical designs.

Experience with Well‑Architected Review and remediation.

Deep understanding of AWS services: compute, storage, networking, AI/ML.

Experience designing scalable, highly available, fault‑tolerant systems.

Ability to create clear architecture diagrams and technical documentation.

Cost modeling and ROI calculation capabilities.

Advanced Python programming: OOP, async, testing.

API development with FastAPI, Flask, or similar frameworks.

Frontend basics: React (for prototypes and demos).

Shell scripting for automation and deployment.

Git version control and collaborative development workflows.

AWS CDK (required), CloudFormation experience.

Terraform (highly preferred) for multi‑cloud or hybrid scenarios.

Hands‑on experience with AWS SageMaker: training jobs, endpoints, pipelines, notebooks.

Knowledge of containerization for ML models (Docker).

Familiarity with ML frameworks: PyTorch, TensorFlow, LangChain, Llamaindex.

CI/CD pipeline design and implementation (GitHub Actions, GitLab CI, AWS CodePipeline).

Container orchestration: Docker, Kubernetes, Amazon ECS.

Excellent written and verbal communication in Advanced English.

Experience presenting technical architectures to clients and stakeholders.

Debugging and troubleshooting complex distributed systems.

Incident response and root cause analysis.

Experience

5+ years in cloud engineering, DevOps, or solution architecture roles.

3+ years hands‑on experience with AWS services and architecture.

2+ years working with AI/ML workloads on cloud platforms.

Hands‑on experience deploying and managing ML models in production.

3+ years building infrastructure using IaC tools (AWS CDK, Terraform, CloudFormation).

4+ years programming experience in Python (required).

3+ years implementing CI/CD pipelines and deployment automation.

Experience gathering requirements and translating them into technical solutions.

Consulting or professional services background preferred.

Experience in regulated industries (FinTech, Insurance, Banks) preferred.

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