Senior Software Engineer – AI & Analytics

Infomineo

Senior AI Software Engineer at Infomineo leading AI development and data analytics initiatives. Designing innovative data products and collaborating with global teams for impactful solutions.

Posted 7/8/2026full-timeCasablanca • 🇲🇦 MoroccoSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing AI-powered analytical solutions, with a strong focus on machine learning, LLM integration, and production-grade application development. Proficient in Python and full-stack development, ensuring reliable and scalable AI solutions that meet business needs.

Highest-signal resume keywords

Python ProgrammingMachine Learning ImplementationLLM IntegrationFastAPI DevelopmentCloud Deployment

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Hard Skills

Data ScienceAI DevelopmentGenerative AIModel Context Protocol (MCP)Analytical WorkflowsDockerCI/CD PipelinesPerformance MonitoringPrompt EngineeringVector Database Management

Soft Skills

MentoringCollaborationCommunication

Tools & Technologies

Google Cloud PlatformGitHub ActionsLangChainOpenAIReactNext.jsVue

Industry Keywords

AI SolutionsRAG ArchitecturesData PipelinesEnterprise SaaSProduction Reliability

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformJavaScriptNext.jsPythonReactVue.js

About the role

Key responsibilities & impact

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.
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.
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.
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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.