About The Project Join 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. 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. 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. You’ll work across multiple AI consulting engagements, from Generative AI workshops to enterprise ML platform development, all while maintaining the highest standards of security and compliance required by financial institutions. Duration & Reporting Part‑time long‑term engagement with project‑based allocations. 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 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%) Requirements & Design 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 – 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 – 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 – compute, storage, networking, AI / ML. Experience designing scalable, highly available, 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 – FastAPI, Flask, or similar frameworks. Frontend development basics – React, etc (prototypes, demos). 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 – training / inference. Knowledge of containerization – Docker, registry. Familiarity with ML frameworks – PyTorch, TensorFlow, LangChain, Llamaindex, etc. DevOps & Automation CI / CD pipeline design – GitHub Actions, GitLab CI, AWS CodePipeline. Container orchestration – Docker, Kubernetes, Amazon ECS. Configuration management – deployment automation. Monitoring & observability – CloudWatch, Prometheus, Grafana, ELK stack. Communication & Collaboration Excellent written and verbal communication – Advanced English. Explain complex concepts to non‑technical stakeholders. Client‑facing presentations and technical demos. Strong documentation skills – detail orientation. Collaborative mindset – cross‑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). Highly preferred – Solutions Architect Professional or AWS Machine Learning – Specialty. Deep core AWS services knowledge – compute, storage, networking, AI / ML. AI / ML Technologies Machine learning concepts – model training / deployment lifecycle. Generative AI technologies – LLMs, RAG, vector databases, prompt engineering. ML frameworks – PyTorch, TensorFlow, scikit‑learn, Pandas, NumPy. MLOps practices and tools. Model serving patterns – real‑time vs batch inference. Software Development Modern software development practices – testing, code review, documentation. API design – RESTful, GraphQL, event‑driven architectures. SQL and NoSQL database design and optimization. Authentication and authorization – OAuth, JWT, IAM. DevOps & Infrastructure Linux/UNIX system administration. Networking fundamentals – TCP/IP, DNS, HTTP/HTTPS. Security best practices for cloud environments. Disaster recovery and business continuity planning. Industry Knowledge Cloud consulting delivery models. Agile/scrum methodologies. Compliance frameworks – GDPR, HIPAA, SOC2, ISO27001. FinTech, 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 – cloud engineering, DevOps, solution architecture. 3 years hands‑on experience – AWS services, architecture. Track record designing and implementing cloud solutions. Greenfield projects and 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. GPU‑based compute – training / inference. AI / ML infrastructure challenges and optimization techniques. Infrastructure as Code 3 years building infrastructure – IaC tools (AWS CDK, Terraform, CloudFormation). Experience creating reusable IaC modules and components. Track record 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 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 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. J-18808-Ljbffr