AI & Analytics - Sr. Software Engineer With AI Background - Cairo

About us Infomineo is a pioneering global AI-enhanced research company that transforms how businesses access, analyze, and act on critical intelligence. We’ve evolved from traditional business research outsourcing to become the strategic partner that combines cutting-edge artificial intelligence with deep human expertise. We offer 3 services to our global clients (leading consulting companies, Fortune 500 companies, and government entities): AI and Data Advisory, Next-Gen Insights and Resource Scaling. This is made possible by relying on 3 pillars of excellence: 350+ industry experts spread across 5 offices (Cairo, Casablanca, Mexico City, Dubai, Barcelona). Our proprietary AI orchestrator. Extensive knowledge assets combining 500,000+ delivered case studies and database subscriptions. Ready to kick start your career with us? Why Infomineo? Here’s what sets us apart: Shape the Future of Business Insights: You will be at the forefront, leading the design and implementation of AI-driven solutions that automate tasks and drive efficiency across our entire service spectrum (Business Research, Content, Design, and Data Analytics). Work with Global Leaders: Our clients are industry leaders — Fortune 500s, top consultancies, governments, and NGOs. You will take ownership of delivering technical solutions that directly support their success. Lead in AI & Technology: We foster continuous learning and technical excellence. You will stay ahead of the latest advancements in AI and software engineering, and actively drive innovation across the team. Thrive in a Collaborative Culture: We value intellectual curiosity, leadership, and a can-do attitude. You will be encouraged to mentor others, contribute strategic ideas, and make a lasting impact on the company’s growth. About this role: We are seeking an experienced and technically strong individual to join us as Senior AI Software Engineer. In this role, you will take ownership of designing, developing, and deploying AI-powered data products and analytical applications that serve our internal teams and global clients. You will lead data science and AI initiatives, define best practices for applied AI and analytical solution development, and collaborate closely with engineering, product, and business stakeholders. While the role is primarily data science and AI-focused, you will also leverage your full-stack development background to build production-ready applications, integrate AI capabilities, and support cloud-based deployment. Key Responsibilities: Data Science, AI & Applied R&D: Lead the design and development of AI-powered analytical solutions, data products, and intelligent applications that solve complex business problems. Translate business and client requirements into data science approaches, AI workflows, and scalable technical solutions. Design, prototype, and productionize machine learning, LLM, and generative AI solutions with a focus on business value, reliability, and usability. Own the architecture of Retrieval-Augmented Generation (RAG) pipelines, including document processing, vectorization, semantic search, evaluation, and query optimization for enterprise use cases. Design and implement complex AI-powered features by integrating LLM APIs and services using frameworks such as LangChain or equivalent, with a focus on reliability, accuracy, and performance in production. Design, implement, and maintain Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and data sources, enabling context-aware and extensible AI solutions. Develop evaluation frameworks, monitoring approaches, and observability practices for LLM-powered systems to ensure quality, transparency, and continuous improvement. Apply advanced prompt engineering, embedding strategies, and vector database management techniques to improve the performance of AI solutions. Integrate AI outputs into analytical workflows, dashboards, reporting tools, and client delivery pipelines. Full-Stack Application Development: Design and contribute to the development of production-grade AI and data applications, primarily using Python and backend frameworks such as FastAPI. Build or support frontend interfaces using modern frameworks such as React, Next.js, or Vue to make AI and data products accessible to business users and clients. Collaborate with software engineers to define scalable application architectures, API standards, and integration patterns. Develop and maintain REST API integrations with third-party AI services, enterprise SaaS platforms, internal tools, and external data sources. Ensure that data science prototypes are translated into maintainable, secure, and scalable production solutions. Participate in code reviews, define technical best practices, and contribute to a high-quality engineering and data science culture. Cloud, Deployment & MLOps: Support the containerization and cloud deployment of AI and data applications, preferably on Google Cloud Platform using GKE and Artifact Registry, while remaining adaptable to other cloud environments. Design and maintain CI/CD pipelines using GitHub Actions or equivalent tools to ensure reliable and repeatable releases. Apply MLOps and LLMOps practices to manage experimentation, deployment, monitoring, and continuous improvement of AI systems. Collaborate with engineering and infrastructure teams to ensure the reliability, scalability, and performance of production environments. Proactively identify performance bottlenecks in AI workflows, data pipelines, application layers, and infrastructure. Technical Leadership & Collaboration: Lead applied AI and data science initiatives from discovery and prototyping through production deployment. Mentor junior data scientists, AI engineers, and developers on data science methods, AI integration, coding practices, and production readiness. Work closely with product teams, consultants, analysts, and non-technical stakeholders to ensure solutions are aligned with business needs. Define standards and best practices for AI solution design, evaluation, documentation, and delivery. Communicate complex technical concepts clearly to both technical and non-technical audiences. Qualifications: 4 to 6 years of experience in data science, AI development, applied machine learning, or related technical roles, with hands-on experience delivering production-grade AI or data products. Strong proficiency in Python, with experience using data science, machine learning, and AI libraries and frameworks. Solid full-stack development background, including experience with backend frameworks such as FastAPI and modern frontend frameworks such as React, Next.js, or Vue. Deep understanding of LLMs, RAG architectures, generative AI workflows, and production-grade AI service integration, including tools such as OpenAI, Gemini, LangChain, or equivalent. Proven experience designing and implementing Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and enterprise data sources. Experience building analytical workflows, dashboards, data pipelines, or AI-powered decision-support tools in a client delivery or enterprise context. Hands-on experience with Docker and cloud deployment on at least one major cloud platform such as GCP, AWS, Azure, or equivalent. Familiarity with container orchestration, artifact management, CI/CD pipelines, GitHub Actions, GitOps workflows, and branching strategies. Strong understanding of LLM observability, AI evaluation, performance monitoring, and production reliability practices. Demonstrated ability to lead technical initiatives, mentor junior team members, and collaborate effectively with product teams and business stakeholders. Bachelor’s or Master’s degree in Data Science, Computer Science, Software Engineering, Statistics, Applied Mathematics, or a related field. Preferred Skills: Experience with agentic AI frameworks such as LangGraph or similar orchestration tools for building multi-step AI workflows. Knowledge of advanced prompt engineering, vector database management, embedding model optimization, and AI evaluation techniques. Experience with MLOps or LLMOps practices, including experiment tracking, model monitoring, and AI quality evaluation. Experience with Infrastructure as Code tools such as Terraform or Pulumi. Experience designing data models, analytical pipelines, or BI/dashboard solutions. Relevant certifications such as Google Cloud Professional Data Engineer, Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Developer, or similar cloud and AI credentials. What we offer: A competitive compensation and benefits package. The opportunity to lead AI, data science, and technology initiatives with real global impact. A dynamic and supportive work environment that values leadership, innovation, and your contributions. Continuous learning and professional development opportunities to propel your career forward in AI, data science, and technology. Application Process: Candidates are invited to submit a resume, cover letter, and any relevant portfolio, GitHub links, or project examples showcasing their experience in data science, AI solution development, full-stack application development, and cloud deployment. Shortlisted candidates will undergo a technical assessment and interview to demonstrate their data science expertise, AI implementation skills, full-stack capabilities, and technical leadership potential. Infomineo: Where brilliant minds meet to shape the future of business.