AI Cloud Solution Architect & Engineer

About The Project

Joinurons Lab as an AI Cloud Solution Architect & Engineer – a unique hybrid role combining strategic solution design with hands‑on engineering execution. You’ll bridge the gap between client requirements and technical implementation, designing AI/ML architectures and then building them yourself using modern cloud infrastructure practices.

Our Focus: We specialize in serving Banking, Financial Services, and Insurance (BFSI) enterprise customers with stringent compliance, security, and regulatory requirements. You’ll work on mission‑critical AI/ML systems where security architecture, data governance, and regulatory compliance are paramount.

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

Reporting: Direct report to Head of Cloud

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 Ia
  C, 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

KPI – Engineering Delivery

• Deploy infrastructure through Ia
  C (AWS CDK/Terraform) with zero manual configuration
• Create at least 3 reusable Ia
  C 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

KPI – 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 Po
  C 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 Planningh3>

• 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%)

• Build and maintain cloud infrastructure using AWS CDK (primary) and Terraform
• Develop reusable Ia
  C 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, Sage
  Maker compute instances

Application Development

• Develop Python applications: Fast
  API 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 (Sage
  Maker endpoints, containerized models)
• Build ML pipelines: data ingestion, preprocessing, training automation, model deployment
• Implement model versioning, experiment tracking, and A/B testing frameworks
• Monitor model performance, inference latency, and system health metrics

Dev
Ops & Automation

• Design and implement CI/CD pipelines using Git
  Hub Actions, Git
  Lab CI, or AWS Code
  Pipeline
• Automate deployment processes with infrastructure testing and validation
• Implement monitoring, logging, and alerting using Cloud
  Watch, 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 Fast
  API, 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) – Cloud
  Formation experience is valuable
• Terraform (highly preferred) for multi‑cloud or hybrid scenarios
• Understanding of Ia
  C best practices: modularity, reusability, testing
• Experience with infrastructure testing and validation frameworks

AI/ML Infrastructure

• Hands‑on experience with AWS Sage
  Maker: 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: Py
  Torch, Tensor
  Flow, Lang
  Chain, Llama
  Index, etc

Dev
Ops & Automation

• CI/CD pipeline design and implementation (Git
  Hub Actions, Git
  Lab CI, AWS Code
  Pipeline)
• Container orchestration: Docker, Kubernetes, Amazon ECS
• Configuration management and deployment automation
• Monitoring and observability: Cloud
  Watch, Prometheus, Grafana, ELK stack

Communication & Collaboration

• Excellent written and verbal communication 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

Experience – Cloud Engineering & Architecture

• 5+ years in cloud engineering, Dev
  Ops, 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

Experience – 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

Experience – Infrastructure as Code

• 3+ years building infrastructure using Ia
  C tools (AWS CDK, Terraform, Cloud
  Formation)
• Experience creating reusable Ia
  C modules and components
• Track record of infrastructure automation and standardization

Experience – Software Development

• 4+ years programming experience in Python (required)
• Experience building APIs with Fast
  API, Flask, or similar frameworks
• History of creating prototypes, MVPs, or Po
  C applications
• Comfortable with full‑stack development for demos and prototypes

Experience – Dev
Ops & 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

Experience – 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

Experience – Industry (Preferred)

• Consulting or professional services background
• Experience in regulated industries (Fin
  Tech, Insurance, Banks)
• Work with enterprise clients on large‑scale implementations
• Startup or fast‑paced environment experience

Seniority level

Mid‑Senior level

Employment type

Part‑time

Job function

Engineering and Information Technology

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.