Senior AI Infrastructure Engineer

About UsAt IdeaSouq, we are building the"AI operating system"to transform how private market investors and funds discover, evaluate, and manage their opportunities. Traditional investment workflows are drowning in data silos, manual screening, and overwhelming deal flow. We're a startup building the solution: an AI analyst that turns this deal flow chaos into funding intelligence. Our platform provides smart sourcing, deep evaluation based on a fund's unique thesis, agentic workflows to find missing data, and seamless portfolio monitoring, all while built on a foundation ofenterprise-grade securityanddata sovereignty . We are looking for a foundational engineer to join our small, high-impact team. If you are passionate about building secure, scalable, and self-evolving AI systems from the ground up, this is your opportunity to define the future of investment technology.The RoleAs our Senior AI Infrastructure Engineer (Enterprise Cloud & AIOps), you will be the chief architect and hands-on builder of the infrastructure that powers our entire AI platform. You willown the deployment, optimization, and operational excellenceof the core AI models and agentic workflows that are our product. This is a critical, high-ownership role. You will be responsible for building the production-grade MLOps pipelines, secure back-end services, and AIOps frameworks. You will ensure our platform meets the "demanding investment environments" of our clients by guaranteeing scalability, reliability, and our promise of data sovereignty.Core ResponsibilitiesArchitect Enterprise AI Systems:Design and build the scalable, secure back-end microservices (Python/FastAPI) that serve our "AI analyst," agentic workflows, and custom-tuned LLMs. Lead MLOps Implementation:Build and own the end-to-end MLOps lifecycle. This includes automated CI/CD for model deployment, model versioning, and creating pipelines forfine-tuning models to each client's unique investment thesis .Cloud Deployment & Orchestration:Deploy, scale, and manage our AI services usingDockerandKubernetes (AKS, EKS) . You will be the primary owner of ourInfrastructure as Code (Terraform) , responsible for provisioning all cloud resources. Secure Multi-Cloud Architecture:Implement and manage ourdata sovereigntystrategy acrossAWS and Azure , ensuring sensitive client data remains secure, segregated, and compliant. Performance & Cost Optimization:Relentlessly profile and optimize our AI models (e.g., quantization, inference speed) and infrastructure for latency, throughput, and cost. Implement serverless (Lambda/Azure Functions) and auto-scaling logic. Develop AIOps Solutions:Build and manage a comprehensive monitoring, logging, and alerting framework ( CloudWatch, Azure Monitor, Prometheus ).Key Qualifications & SkillsWe are looking for a seasoned, hands-on expert who thrives in a fast-paced startup environment and has a track record of running large-scale AI systems in production.Required Experience: 7+ yearsof professional software engineering experience, with at least3+ years in a senior or lead rolefocusing on cloud infrastructure, DevOps, or MLOps. Demonstrable, hands-on expertise inarchitecting and deploying production AI/ML workloads on both AWS and Azure . Proven experience building and maintaining high-availability, secure back-end systems. The ability to work independently, make critical technical decisions, and build systems from the ground up.Technical Skillset: Back-End:Deep expertise inPythonand frameworks likeFastAPI , Flask, or Django. AI / ML: Strong practical knowledge ofPyTorchorTensorFlow . Experience deployingGenerative AI (LLMs)and RAG systems in production. Proficiency with MLOps frameworks (e.g.,MLflow , Kubeflow). Databases:Expertise with both SQL and NoSQL databases. MLOps, DevOps & AIOps: Orchestration/:Expert-levelDocker&Kubernetes (AKS, EKS) . Deep proficiency withTerraform(preferred) or CloudFormation/Bicep. CI/CD:Mastery ofAzure DevOps (Pipelines)orAWS CodePipeline/ GitHub Actions. Monitoring (AIOps):Hands-on experience withAWS CloudWatch ,Azure Monitor , Prometheus, and Grafana. Logging:Experience withAmazon OpenSearch ServiceorAzure Log Analytics