Senior AI Infrastructure Engineer

About Us At 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 of enterprise-grade security and data 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 Role As 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 will own the deployment, optimization, and operational excellence of 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 Responsibilities Architect 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 for fine-tuning models to each client‘s unique investment thesis . Cloud Deployment & Orchestration: Deploy, scale, and manage our AI services using Docker and Kubernetes (AKS, EKS) . You will be the primary owner of our Infrastructure as Code (Terraform) , responsible for provisioning all cloud resources. Secure Multi-Cloud Architecture: Implement and manage our data sovereignty strategy across AWS 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 & Skills We 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+ years of professional software engineering experience, with at least 3+ years in a senior or lead role focusing on cloud infrastructure, DevOps, or MLOps. Demonstrable, hands-on expertise in architecting 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 in Python and frameworks like FastAPI , Flask, or Django. AI / ML: Strong practical knowledge of PyTorch or TensorFlow . Experience deploying Generative 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-level Docker & Kubernetes (AKS, EKS) . Deep proficiency with Terraform (preferred) or CloudFormation/Bicep. CI/CD: Mastery of Azure DevOps (Pipelines) or AWS CodePipeline / GitHub Actions. Monitoring (AIOps): Hands-on experience with AWS CloudWatch , Azure Monitor , Prometheus, and Grafana. Logging: Experience with Amazon OpenSearch Service or Azure Log Analytics