About Intellibus Intellibus designs and engineers technology platforms for established FinTech and financial-services firms. We build production-grade systems that help organizations solve complex business problems through software engineering, data, and emerging technologies. We are looking for engineers who can turn AI capabilities into practical, reliable applications that deliver measurable business value. The AI Engineer Role We are hiring hands-on AI Engineers to design, develop, integrate, and deploy AI-powered applications using large language models (LLMs), Python, prompt engineering, SQL, and modern data platforms. You will work across the AI application lifecycle, from understanding business requirements and preparing data to building LLM integrations, evaluating outputs, and deploying production-ready solutions. This is an engineering role, not a research-only or strategy-only position. We need candidates who can write code, work directly with data, troubleshoot technical issues, and deliver functioning applications. Experience with Microsoft Azure and Microsoft Fabric is especially relevant. What You Will Do AI & LLM Engineering Build and integrate applications powered by large language models. Develop, test, and refine prompts to improve response quality, consistency, and relevance. Design reusable AI workflows and integrate LLM capabilities into existing enterprise applications. Evaluate model outputs, manage edge cases, and improve the reliability of AI-generated responses. Apply appropriate validation, security, and error-handling practices. Python Development Develop production-quality Python applications, services, and automation. Build APIs and backend components that connect AI models, data sources, and enterprise systems. Write maintainable code, perform debugging, and contribute to testing and deployment. Data Engineering & Modeling Work with SQL, relational data, and structured or unstructured data sources. Develop data models and data-processing workflows that support AI applications. Prepare, validate, transform, and retrieve data for LLM-based solutions. Use Microsoft Fabric and Azure data services to support data integration and AI workflows. Azure & Production Delivery Develop and deploy AI-enabled solutions using Microsoft Azure. Integrate AI services with enterprise applications, APIs, and data platforms. Monitor application performance, troubleshoot issues, and optimize reliability. Collaborate with engineering and business teams to deliver working solutions against defined requirements. What Sets You Apart Hands-on experience building applications using Python. Practical experience working with LLMs, generative AI, and prompt engineering. Understanding how to integrate AI capabilities into real applications rather than relying solely on AI tools. Strong SQL skills and an understanding of data modeling. Experience with Microsoft Azure and, ideally, Microsoft Fabric. Ability to work with APIs, data pipelines, and enterprise application integrations. Understanding of testing, debugging, and production deployment. Ability to explain technical decisions and deliver solutions independently. Technology Ecosystem AI: LLMs, generative AI, prompt engineering, AI application development, model evaluation Programming: Python, APIs, backend development Data: SQL, data modeling, data transformation, data integration Cloud: Microsoft Azure, Azure AI services Data Platform: Microsoft Fabric Engineering Practices: Testing, debugging, deployment, monitoring, production support Our Process Initial Screening: 15-minute video call with the Recruiting Team. Assessments: One proctored GQ multiple-choice assessment and a SlideWare presentation (under 2 hours). Interview Rounds: Three 30-minute video interviews with the Technical Team, Project Lead, and Executive Team. Offer: Final decision and job offer. If this sounds like the kind of work you want to be doing, apply now. Our team will be in touch within the hour.