Ph3About The Project /h3 pJoin Neurons 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. /p h3Our Focus /h3 pWe 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. /p h3Duration /h3 pPart-time long-term engagement with project-based allocations /p h3Reporting /h3 pDirect report to Head of Cloud /p h3Objective /h3 pDeliver end-to-end AI cloud solutions by combining architectural excellence with hands-on engineering capabilities : /p ul liArchitecture Design : Gather requirements, design cloud architectures, calculate ROI, and create technical proposals for AI / ML solutions /li liEngineering Excellence : Build production-grade infrastructure using IaC, develop APIs and prototypes, implement CI / CD pipelines, and manage AI workload operations /li liClient Success : Transform business requirements into working solutions that are secure, scalable, cost-effective, and aligned with AWS best practices /li liKnowledge Transfer : Create reusable artifacts, comprehensive documentation, and architectural patterns that accelerate future project delivery /li /ul h3KPI /h3 h3Architecture Pre-Sales /h3 ul liDesign and document 3+ solution architectures per month with comprehensive diagrams and specifications /li liAchieve 80%+ client acceptance rate on proposed architectures and estimates /li liDeliver ROI calculations and cost models within 2 business days of request /li /ul h3Engineering Delivery /h3 ul liDeploy infrastructure through IaC (AWS CDK / Terraform) with zero manual configuration /li liCreate at least 3 reusable IaC components or architectural patterns per quarter /li liImplement CI / CD pipelines for all projects with automated testing and deployment /li liMaintain 95%+ uptime for production AI / ML inference endpoints /li liDocument architecture and implementation details weekly for knowledge sharing /li /ul h3Quality Best Practices /h3 ul liEnsure all solutions pass AWS Well-Architected Review standards /li liDeliver comprehensive documentation within 1 week of architecture completion /li liCreate simplified UIs / demos for PoC validation and client presentations /li /ul h3Areas of Responsibility /h3 h3Solution Architecture (40%) /h3 h3Requirements Design /h3 ul liElicit and document business and technical requirements from clients /li liDesign end-to-end cloud architectures for AI / ML solutions (training, inference, data pipelines) /li liCreate architecture diagrams, technical specifications, and implementation roadmaps /li liEvaluate technology options and recommend optimal AWS services for specific use cases /li /ul h3Business Analysis /h3 ul liCalculate ROI, TCO, and cost-benefit analysis for proposed solutions /li liEstimate project scope, timelines, team composition, and resource requirements /li liParticipate in presales activities: technical presentations, demos, and proposal support /li liCollaborate with sales team on SOW creation and customer workshops /li /ul h3Strategic Planning /h3 ul liDesign for scalability, security, compliance, and cost optimization from day one /li liCreate reusable architectural patterns and reference architectures /li liStay current with AWS AI / ML services and emerging cloud technologies /li /ul h3Cloud Engineering AI Infrastructure (60%) /h3 h3Infrastructure as Code Development /h3 ul liBuild and maintain cloud infrastructure using AWS CDK (primary) and Terraform /li liDevelop reusable IaC components and modules for common patterns /li liImplement infrastructure for AI / ML workloads: GPU clusters, model serving, data lakes /li liManage compute resources: EC2, ECS, EKS, Lambda, SageMaker compute instances /li /ul h3Application Development /h3 ul liDevelop Python applications: FastAPI backends, data processing scripts, automation tools /li liCreate prototype interfaces using Streamlit, React, or similar frameworks /li liBuild and integrate RESTful APIs for AI model serving and data access /li liImplement authentication, authorization, and API security best practices /li /ul h3AI / ML Operations (MLOps) /h3 ul liDeploy and manage AI / ML model serving infrastructure (SageMaker endpoints, containerized models) /li liBuild ML pipelines: data ingestion, preprocessing, training automation, model deployment /li liImplement model versioning, experiment tracking, and A/B testing frameworks /li liManage GPU resource allocation, training job scheduling, and compute optimization /li liMonitor model performance, inference latency, and system health metrics /li /ul h3DevOps Automation /h3 ul liDesign and implement CI / CD pipelines using GitHub Actions, GitLab CI, or AWS CodePipeline /li liAutomate deployment processes with infrastructure testing and validation /li liImplement monitoring, logging, and alerting using CloudWatch, Prometheus, Grafana /li liManage containerization with Docker and orchestration with Kubernetes / ECS /li /ul h3Data Engineering /h3 ul liBuild data pipelines for AI training and inference using AWS Glue, Step Functions, Lambda /li liDesign and implement data lakes using S3, Lake Formation, and data cataloging /li liImplement automated and scheduled data synchronization processes /li liOptimize data storage and retrieval for ML workloads /li /ul h3Security Compliance /h3 ul liImplement cloud security best practices: IAM, VPC design, encryption, secrets management /li liBuild enterprise security and compliance strategies for AI / ML workloads /li liEnsure solutions meet regulatory requirements (PCI-DSS, GDPR, SOC2, MAS TRM, etc where applicable) /li liConduct security reviews and implement remediation strategies /li /ul h3Cost Performance Optimization /h3 ul liOptimize cloud spend for compute-intensive AI workloads /li liImplement spot instance strategies, auto-scaling, and resource scheduling /li liMonitor and optimize GPU utilization, inference latency, and throughput /li liPerform cost analysis and implement cost-saving measures /li /ul h3Operations Support /h3 ul liImplement disaster recovery procedures for AI models and training data /li liManage backup strategies and business continuity planning /li liTroubleshoot and resolve production issues in AI infrastructure /li liProvide technical guidance to project teams during implementation /li /ul h3Skills /h3 h3Cloud Architecture Design /h3 ul liStrong solution architecture skills with ability to translate business requirements into technical designs /li liExperience in Well Architected review and remediation /li liDeep understanding of AWS services, particularly compute, storage, networking, and AI / ML services /li liExperience designing scalable, highly available, and fault-tolerant systems /li liAbility to create clear architecture diagrams and technical documentation /li liCost modeling and ROI calculation capabilities /li /ul h3Technical Leadership /h3 ul liComfortable leading technical discussions with clients and stakeholders /li liAbility to guide engineers and share knowledge effectively /li liStrong problem-solving and analytical thinking skills /li liExperience with architectural decision-making and trade-off analysis /li /ul h3Programming Development /h3 ul liAdvanced Python programming: object-oriented design, async programming, testing /li liAPI development with FastAPI, Flask, or similar frameworks /li liFrontend development basics: React, etc (for prototypes and demos with AI code generation tools) /li liShell scripting for automation and deployment /li liGit version control and collaborative development workflows /li /ul h3Infrastructure as Code /h3 ul liAWS CDK (required) - CloudFormation experience is valuable /li liTerraform (highly preferred) for multi-cloud or hybrid scenarios /li liUnderstanding of IaC best practices: modularity, reusability, testing /li liExperience with infrastructure testing and validation frameworks /li /ul h3AI / ML Infrastructure /h3 ul liHands-on experience with AWS SageMaker: training jobs, endpoints, pipelines, notebooks /li liUnderstanding of ML lifecycle: data preparation, training, deployment, monitoring /li liExperience with GPU management and optimization for training / inference /li liKnowledge of containerization for ML models (Docker, container registries) /li liFamiliarity with ML frameworks: PyTorch, TensorFlow, LangChain, Llamaindex, etc /li /ul h3DevOps Automation /h3 ul liCI / CD pipeline design and implementation (GitHub Actions, GitLab CI, AWS CodePipeline) /li liContainer orchestration: Docker, Kubernetes, Amazon ECS /li liConfiguration management and deployment automation /li liMonitoring and observability: CloudWatch, Prometheus, Grafana, ELK stack /li /ul h3Communication Collaboration /h3 ul liExcellent written and verbal communication in Advanced English /li liAbility to explain complex technical concepts to non-technical stakeholders /li liComfortable with client-facing presentations and technical demos /li liStrong documentation skills with attention to detail /li liCollaborative mindset with ability to work across functional teams /li /ul h3Problem-Solving /h3 ul liAdvanced task breakdown and estimation abilities /li liDebugging and troubleshooting complex distributed systems /li liPerformance optimization and tuningIncident response and root cause analysis /li /ul h3Knowledge /h3 h3AWS Cloud Platform (Required) /h3 ul liAWS Certified Solutions Architect Associate (minimum requirement) /li liAWS Certified Solutions Architect Professional or AWS Certified Machine Learning - Specialty (highly preferred) /li liDeep knowledge of core AWS services: /li liCompute: EC2, Lambda, ECS, EKS, SageMaker /li liStorage: S3, EFS, EBS, FSx /li liNetworking: VPC, Route53, CloudFront, API Gateway, Load Balancers /li liAI / ML: SageMaker, Bedrock, Rekognition, Textract, Comprehend, Lex, Polly /li liData: RDS, DynamoDB, Redshift, Glue, Athena, Kinesis /li liSecurity: IAM, KMS, Secrets Manager, Security Hub, GuardDuty /li liDevOps: GitHub Action, CodePipeline, CodeBuild, CodeDeploy, CloudFormation, CDK, Terraform /li /ul h3AI / ML Technologies /h3 ul liUnderstanding of machine learning concepts and model training / deployment lifecycle /li liFamiliarity with Generative AI technologies: LLMs, RAG, vector databases, prompt engineering /li liKnowledge of ML frameworks and libraries: PyTorch, TensorFlow, scikit-learn, pandas, numpy /li liExperience with MLOps practices and tools /li liUnderstanding of model serving patterns: real-time vs batch inference /li /ul h3Software Development /h3 ul liModern software development practices: testing, code review, documentation /li liAPI design principles: RESTful, GraphQL, event-driven architectures /li liDatabase design and optimization: SQL and NoSQL /li liAuthentication and authorization: OAuth, JWT, IAM /li /ul h3DevOps Infrastructure /h3 ul liLinux / UNIX system administration /li liNetworking fundamentals: TCP / IP, DNS, HTTP / HTTPS, load balancing /li liSecurity best practices for cloud environments /li liDisaster recovery and business continuity planning /li /ul h3Industry Knowledge /h3 ul liUnderstanding of cloud consulting delivery models /li liFamiliarity with agile / scrum methodologies /li liAwareness of compliance frameworks: GDPR, HIPAA, SOC2, ISO27001 /li liKnowledge of FinTech, or other regulated industries (plus) /li /ul h3Additional Knowledge (Preferred) /h3 ul liAzure or GCP certifications and experience /li liMulti-cloud architecture patterns /li liServerless architecture patterns /li liData engineering and data lake design /li liCost optimization strategies and FinOps practices /li /ul h3Experience /h3 h3Cloud Engineering Architecture /h3 ul li5+ years in cloud engineering, DevOps, or solution architecture roles /li li3+ years hands‑on experience with AWS services and architecture /li liProven track record of designing and implementing cloud solutions from scratch /li liExperience with both greenfield projects and cloud migration initiatives /li /ul h3AI / ML Infrastructure /h3 ul li2+ years working with AI / ML workloads on cloud platforms /li liHands‑on experience deploying and managing ML models in production /li liExperience with GPU-based compute for training or inference /li liUnderstanding of AI / ML infrastructure challenges and optimization techniques /li /ul h3Infrastructure as Code /h3 ul li3+ years building infrastructure using IaC tools (AWS CDK, Terraform, CloudFormation) /li liExperience creating reusable IaC modules and components /li liTrack record of infrastructure automation and standardization /li /ul h3Software Development /h3 ul li4+ years programming experience in Python (required) /li liExperience building APIs with FastAPI, Flask, or similar frameworks /li liHistory of creating prototypes, MVPs, or PoC applications /li liComfortable with full-stack development for demos and prototypes /li /ul h3DevOps Automation /h3 ul li3+ years implementing CI / CD pipelines and deployment automation /li liExperience with containerization (Docker) and orchestration (Kubernetes / ECS) /li liLinux / UNIX system administration experience /li liMonitoring and observability implementation /li /ul h3Client-Facing Work /h3 ul liExperience gathering requirements and translating them into technical solutions /li liHistory of presenting technical architectures to clients and stakeholders /li liParticipation in presales activities, demos, or technical workshops /li liAbility to work directly with customers to solve complex problems /li /ul h3Industry Experience (Preferred) /h3 ul liConsulting or professional services background /li liExperience in regulated industries (FinTech, Insurance, Banks) /li liWork with enterprise clients on large-scale implementations /li liStartup or fast-paced environment experience /li /ul /p #J-18808-Ljbffr