AI Cloud Solution Architect & Engineer

Overview

AI Cloud Solution Architect & Engineer at Neurons Lab — a unique hybrid role combining strategic solution design with hands-on engineering execution. You’ll bridge client requirements and technical implementation, designing AI/ML architectures and building them on modern cloud infrastructure practices. About the Project

— Join Neurons Lab as an AI Cloud Solution Architect & Engineer, a role spanning architecture and active engineering across multiple AI engagements. Focus on security, compliance, and regulatory requirements for BFSI clients. Duration : Part-time long-term engagement with project-based allocations Reporting : Direct report to Head of Cloud Objective Deliver end-to-end AI cloud solutions by combining architectural excellence with hands-on engineering capabilities: 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, comprehensive documentation, and architectural patterns that accelerate future project delivery KPI Architecture & Pre-Sales: Design and document 3+ solution architectures per month with 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%) Requirements & Design : Elicit and document business and technical requirements; design end-to-end cloud architectures for AI/ML solutions; create architecture diagrams, technical specifications, and implementation roadmaps; evaluate technology options and recommend optimal AWS services Business Analysis : Calculate ROI, TCO, and cost-benefit analysis; estimate project scope, timelines, team composition, and resource requirements; participate in presales activities; collaborate with sales on SOW and client workshops Strategic Planning : Design for scalability, security, compliance, and cost optimization; create reusable architectural patterns; stay current with AWS AI/ML services Cloud Engineering & AI Infrastructure (60%) Infrastructure as Code Development : Build and maintain cloud infrastructure using AWS CDK (primary) and Terraform; develop reusable IaC components; implement infrastructure for AI/ML workloads; manage compute resources (EC2, ECS, EKS, Lambda, SageMaker) Application Development : Develop Python applications (FastAPI backends, data processing scripts); prototype interfaces (Streamlit, React); build RESTful APIs for AI model serving and data access; implement authentication and API security AI/ML Operations (MLOps) : Deploy and manage AI/ML model serving infrastructure; build ML pipelines; implement model versioning, experiment tracking, and A/B testing; manage GPU resource allocation DevOps & Automation : Design and implement CI/CD pipelines; automate deployment with testing and validation; monitor and alert with CloudWatch/Prometheus/Grafana; manage Docker and Kubernetes/ECS Data Engineering : Build data pipelines for AI training/inference; design data lakes; automated data synchronization; optimize storage for ML workloads Security & Compliance : Implement cloud security best practices; enterprise security and compliance strategies for AI/ML; regulatory requirements (PCI-DSS, GDPR, SOC2, MAS TRM); conduct security reviews Cost & Performance Optimization : Optimize spend for AI workloads; implement spot strategies and auto-scaling; monitor GPU utilization and latency; perform cost analyses Operations & Support : Disaster recovery, backups, business continuity; troubleshoot production issues; provide technical guidance to project teams Skills Cloud Architecture & Design : Well-Architected reviews, AWS services, architecture diagrams, cost modeling Technical Leadership : Lead technical discussions, guide engineers, mentor; strong problem-solving Programming & Development : Python (advanced), API development (FastAPI/Flask), prototype UI development, RESTful APIs, authentication practices Infrastructure as Code : AWS CDK, Terraform, IaC best practices AI/ML Infrastructure : SageMaker, ML lifecycle, GPU management, containerization DevOps & Automation : CI/CD pipelines, Docker, Kubernetes, monitoring, logging Communication & Collaboration : Advanced English, client-facing, documentation, cross-team collaboration Problem-Solving : Estimation, debugging distributed systems, performance tuning, incident response Knowledge AWS Cloud Platform (Required) : AWS SA Associate (required); SA Professional or ML-Specialty (preferred); deep AWS services knowledge by category AI/ML Technologies : ML lifecycle, Generative AI, ML frameworks, MLOps, model serving patterns Software Development : Modern practices, API design, databases, auth patterns DevOps & Infrastructure : Linux, networking, security, DR/BCP Industry Knowledge : Cloud consulting delivery, agile methods, compliance frameworks; FinTech familiarity Additional Knowledge (Preferred) : Azure/GCP experience, multi-cloud, serverless, data lake design, FinOps Experience Cloud Engineering & Architecture : 5+ years in cloud engineering/DevOps/solution architecture; 3+ years AWS AI/ML Infrastructure : 2+ years with AI/ML workloads on cloud; production ML deployment; GPU experience Infrastructure as Code : 3+ years with IaC (CDK, Terraform, CloudFormation) Software Development : 4+ years Python; API frameworks; prototypes DevOps & Automation : 3+ years CI/CD; Docker/Kubernetes; Linux admin Client-Facing Work : Requirements gathering; present architectures; presales Industry Experience (Preferred) : Consulting background; FinTech/regulated industries; large-scale enterprise work Seniority level

Mid-Senior level Employment type

Part-time Job function

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