AI Cloud Solution Architect

Overview Join Neurons Lab as an AI Cloud Solution Architect & Engineer — a unique hybrid role combining strategic solution design with hands-on engineering execution. You\u2019ll bridge the gap between client requirements and technical implementation, designing AI/ML architectures and then building them using modern cloud infrastructure practices. About The Project : Join Neurons Lab as an AI Cloud Solution Architect & Engineer, delivering end-to-end AI cloud solutions with architectural excellence and hands-on engineering capabilities for BFSI clients, emphasizing security, data governance, and regulatory compliance. Our Focus : Banking, Financial Services, and Insurance (BFSI) enterprise customers with stringent compliance, security, and regulatory requirements. You\u2019ll work on mission-critical AI/ML systems, maintaining high standards of security and compliance for financial institutions. 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 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, comprehensive documentation, and architectural patterns to accelerate future projects KPIs Architecture & Pre-Sales: Design and document multiple architectures with diagrams and specifications; achieve high client acceptance rates; deliver ROI calculations within 2 business days Engineering Delivery: Deploy infrastructure via IaC (AWS CDK/Terraform) with minimal manual configuration; maintain uptime; create reusable IaC components; implement CI/CD pipelines Quality & Best Practices: Ensure AWS Well-Architected compliance; deliver documentation within 1 week; provide demos for PoCs Areas of Responsibility Solution Architecture (40%) Requirements & Design: Elicit business/technical requirements; design end-to-end architectures; create diagrams and roadmaps; evaluate tech options Business Analysis: ROI/TCO, scope estimation, presales participation, SOW collaboration Strategic Planning: Design for scalability, security, compliance, and cost optimization; reusable patterns; stay current with AWS AI/ML services Cloud Engineering & AI Infrastructure (60%) Infrastructure as Code Development: AWS CDK (primary) and Terraform; reusable IaC components; AI/ML workloads infrastructure Application Development: Python apps, prototypes, RESTful APIs; authentication and API security AI/ML Operations (MLOps): Model serving, pipelines, versioning, experiment tracking, GPU resource management DevOps & Automation: CI/CD pipelines, monitoring/logging (CloudWatch, Prometheus, Grafana); containerization and orchestration Data Engineering: Data pipelines, data lakes, data synchronization, storage optimization Security & Compliance: IAM, encryption, regulatory requirements (PCI-DSS, GDPR, SOC2, MAS TRM where applicable) Cost & Performance Optimization: Cloud spend, auto-scaling, GPU utilization, cost-saving measures Operations & Support: DR, backups, production issue resolution, project team guidance Skills & Knowledge Cloud Architecture & Design : Solution architecture, Well-Architected reviews, AWS services, scalable and cost-optimized designs, architecture diagrams Technical Leadership : Leading technical discussions; guiding engineers; strong problem-solving Programming & Development : Python (advanced), API development, prototype UI/UX for demos, API security Infrastructure as Code : AWS CDK, Terraform, IaC best practices, testing AI/ML Infrastructure : SageMaker, GPUs, containerization, ML lifecycle, PyTorch/TensorFlow DevOps & Automation : CI/CD (GitHub Actions, GitLab CI, CodePipeline), Docker/Kubernetes, monitoring Communication & Collaboration : English proficiency, client-facing presentations, documentation, cross-team collaboration Problem-Solving : Task breakdown, debugging distributed systems, performance tuning Knowledge & Certifications AWS Cloud Platform (Required) : AWS Certified Solutions Architect Associate (minimum); AWS Architect Professional or AWS ML Specialty highly preferred Deep AWS services knowledge across Compute, Storage, Networking, AI/ML, Data, Security, DevOps AI/ML Technologies ML concepts, model training/deployment lifecycle; Generative AI technologies; ML frameworks (PyTorch, TensorFlow, scikit-learn) ML/Ops practices and real-time vs batch inference patterns Industry Experience FinTech, regulated industries familiarity; consulting or professional services background is a plus Experience Cloud Engineering & Architecture: 5+ years; 3+ years hands-on AWS; proven cloud solution design from scratch; greenfield and migration experience AI/ML Infrastructure: 2+ years, production deployment, GPU management IaC: 3+ years with AWS CDK, Terraform, CloudFormation Software Development: 4+ years Python; API开发 with FastAPI/Flask; prototypes and PoCs; full-stack demos DevOps & Automation: 3+ years CI/CD; Docker/Kubernetes; Linux administration; monitoring Client-Facing: Requirements gathering; presenting architectures; presales involvement Employment details Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: IT Services and IT Consulting Note: This refined description preserves the core role information while removing extraneous boilerplate and improving readability. 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