AI Cloud Solution Architect & Engineer

Neurons Lab Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia

AI Cloud Solution Architect & Engineer Join Neurons Lab as an AI Cloud Solution Architect & Engineer – a unique hybrid role combining strategic solution design with hands‑on engineering execution.

Location : Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia

Duration : Part‑time long‑term engagement with project‑based allocations

Reporting : Direct report to Head of Cloud

About the Project We specialize in serving Banking, Financial Services, and Insurance (BFSI) enterprise customers with stringent compliance, security, and regulatory requirements. This role is perfect for technical professionals who love both the “what” and the “how” – architecting elegant solutions and rolling up their sleeves to code, deploy, and optimize them.

Objective

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 working solutions that are secure, scalable, cost‑effective, and aligned with AWS best practices

Knowledge Transfer : Create reusable artifacts, comprehensive documentation, and architectural patterns that accelerate future project delivery

KPI Architecture & Pre‑Sales

Design and document 3+ 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

Engineering Delivery

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

Document architecture and implementation details weekly for knowledge sharing

Quality & Best Practices

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

Areas of Responsibility Solution Architecture (40%)

Elicit and document business and technical requirements from clients

Design end‑to‑end cloud architectures for AI / ML solutions (training, inference, data pipelines)

Create architecture diagrams, technical specifications, and implementation roadmaps

Evaluate technology options and recommend optimal AWS services for specific use cases

Business Analysis

Calculate ROI, TCO, and cost‑benefit analysis for proposed solutions

Estimate project scope, timelines, team composition, and resource requirements

Participate in presales activities : technical presentations, demos, and proposal support

Collaborate with sales team on SOW creation and customer workshops

Strategic Planning

Design for scalability, security, compliance, and cost optimization from day one

Create reusable architectural patterns and reference architectures

Stay current with AWS AI / ML services and emerging cloud technologies

Cloud Engineering & AI Infrastructure (60%) Infrastructure as Code Development

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

Manage compute resources : EC2, ECS, EKS, Lambda, SageMaker compute instances

Application Development

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

Create prototype interfaces using Streamlit, React, or similar frameworks

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

Implement authentication, authorization, and API security best practices

AI / ML Operations (MLOps)

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

Build ML pipelines : data ingestion, preprocessing, training automation, model deployment

Implement model versioning, experiment tracking, and A / B testing frameworks

Manage GPU resource allocation, training job scheduling, and compute optimization

Monitor model performance, inference latency, and system health metrics

DevOps & Automation

Design and implement CI / CD pipelines using GitHub Actions, GitLab CI, or AWS CodePipeline

Automate deployment processes with infrastructure testing and validation

Implement monitoring, logging, and alerting using CloudWatch, Prometheus, Grafana

Manage containerization with Docker and orchestration with Kubernetes / ECS

Data Engineering

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

Design and implement data lakes using S3, Lake Formation, and data cataloging

Implement automated and scheduled data synchronization processes

Optimize data storage and retrieval for ML workloads

Security & Compliance

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

Build enterprise security and compliance strategies for AI / ML workloads

Ensure solutions meet regulatory requirements (PCI-DSS, GDPR, SOC2, MAS TRM, etc where applicable)

Conduct security reviews and implement remediation strategies

Cost & Performance Optimization

Optimize cloud spend for compute-intensive AI workloads

Implement spot instance strategies, auto-scaling, and resource scheduling

Monitor and optimize GPU utilization, inference latency, and throughput

Perform cost analysis and implement cost-saving measures

Operations & Support

Implement disaster recovery procedures for AI models and training data

Manage backup strategies and business continuity planning

Troubleshoot and resolve production issues in AI infrastructure

Provide technical guidance to project teams during implementation

Skills Cloud Architecture & Design

Strong solution architecture skills with ability to translate business requirements into technical designs

Experience in Well‑Architected review and remediation

Deep understanding of AWS services, particularly compute, storage, networking, and AI / ML services

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

Ability to create clear architecture diagrams and technical documentation

Cost modeling and ROI calculation capabilities

Technical Leadership

Comfortable leading technical discussions with clients and stakeholders

Ability to guide engineers and share knowledge effectively

Strong problem‑solving and analytical thinking skills

Experience with architectural decision‑making and trade‑off analysis

Programming & Development

Advanced Python programming : object‑oriented design, async programming, testing

API development with FastAPI, Flask, or similar frameworks

Frontend development basics : React, etc (for prototypes and demos with AI code generation tools)

Shell scripting for automation and deployment

Git version control and collaborative development workflows

Infrastructure as Code

AWS CDK (required) - CloudFormation experience is valuable

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

Understanding of IaC best practices : modularity, reusability, testing

Experience with infrastructure testing and validation frameworks

AI / ML Infrastructure

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

Understanding of ML lifecycle : data preparation, training, deployment, monitoring

Experience with GPU management and optimization for training / inference

Knowledge of containerization for ML models (Docker, container registries)

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

DevOps & Automation

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

Container orchestration : Docker, Kubernetes, Amazon ECS

Configuration management and deployment automation

Monitoring and observability : CloudWatch, Prometheus, Grafana, ELK stack

Communication & Collaboration

Excellent written and verbal communication in Advanced English

Ability to explain complex technical concepts to non‑technical stakeholders

Comfortable with client‑facing presentations and technical demos

Strong documentation skills with attention to detail

Collaborative mindset with ability to work across functional teams

Problem‑Solving

Advanced task breakdown and estimation abilities

Debugging and troubleshooting complex distributed systems

Performance optimization and tuning

Incident response and root cause analysis

Knowledge AWS Cloud Platform (Required)

AWS Certified Solutions Architect Associate (minimum requirement)

AWS Certified Solutions Architect Professional or AWS Certified Machine Learning - Specialty (highly preferred)

Deep knowledge of core AWS services :

Compute : EC2, Lambda, ECS, EKS, SageMaker

Storage : S3, EFS, EBS, FSx

Networking : VPC, Route53, CloudFront, API Gateway, Load Balancers

AI / ML : SageMaker, Bedrock, Rekognition, Textract, Comprehend, Lex, Polly

Data : RDS, DynamoDB, Redshift, Glue, Athena, Kinesis

Security : IAM, KMS, Secrets Manager, Security Hub, GuardDuty

DevOps : GitHub Action, CodePipeline, CodeBuild, CodeDeploy, CloudFormation, CDK, Terraform

AI / ML Technologies

Understanding of machine learning concepts and model training / deployment lifecycle

Familiarity with Generative AI technologies : LLMs, RAG, vector databases, prompt engineering

Knowledge of ML frameworks and libraries : PyTorch, TensorFlow, scikit‑learn, pandas, numpy

Experience with MLOps practices and tools

Understanding of model serving patterns : real‑time vs batch inference

Software Development

Modern software development practices : testing, code review, documentation

API design principles : RESTful, GraphQL, event‑driven architectures

Database design and optimization : SQL and NoSQL

Authentication and authorization : OAuth, JWT, IAM

DevOps & Infrastructure

Linux / UNIX system administration

Networking fundamentals : TCP / IP, DNS, HTTP / HTTPS, load balancing

Security best practices for cloud environments

Disaster recovery and business continuity planning

Industry Knowledge

Understanding of cloud consulting delivery models

Familiarity with agile / scrum methodologies

Awareness of compliance frameworks : GDPR, HIPAA, SOC2, ISO27001

Knowledge of FinTech, or other regulated industries (plus)

Additional Knowledge (Preferred)

Azure or GCP certifications and experience

Multi‑cloud architecture patterns

Serverless architecture patterns

Data engineering and data lake design

Cost optimization strategies and FinOps practices

Experience Cloud Engineering & Architecture

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

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

Proven track record of designing and implementing cloud solutions from scratch

Experience with both greenfield projects and cloud migration initiatives

AI / ML Infrastructure

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

Hands‑on experience deploying and managing ML models in production

Experience with GPU‑based compute for training or inference

Understanding of AI / ML infrastructure challenges and optimization techniques

Infrastructure as Code

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

Experience creating reusable IaC modules and components

Track record of infrastructure automation and standardization

Software Development

4+ years programming experience in Python (required)

Experience building APIs with FastAPI, Flask, or similar frameworks

History of creating prototypes, MVPs, or PoC applications

Comfortable with full‑stack development for demos and prototypes

DevOps & Automation

3+ years implementing CI / CD pipelines and deployment automation

Experience with containerization (Docker) and orchestration (Kubernetes / ECS)

Linux / UNIX system administration experience

Monitoring and observability implementation

Client‑Facing Work

Experience gathering requirements and translating them into technical solutions

History of presenting technical architectures to clients and stakeholders

Participation in presales activities, demos, or technical workshops

Ability to work directly with customers to solve complex problems

Industry Experience (Preferred)

Consulting or professional services background

Experience in regulated industries (FinTech, Insurance, Banks)

Work with enterprise clients on large‑scale implementations

Startup or fast‑paced environment experience

Seniority level Mid‑Senior level

Employment type Full‑time

Job function Engineering and Information Technology

Industries IT Services and IT Consulting

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