مهندس تنفيذ ميداني للذكاء الاصطناعي

Technical Deployment and Integration: Install and configure AI solutions and pipelines in client environments (cloud, on-premises, or hybrid). Connect AI models to existing information systems (APIs, relational and vector databases, ERP, CRM). Implement robust RAG (Retrieval-Augmented Generation) architectures, autonomous agents, and intelligent automation workflows. Integrate AI components into the client's continuous development processes while adhering to SDLC standards (integration into CI/CD pipelines, automated testing, and version control of models and code).

Advanced Support and Incident Resolution: Diagnose and resolve complex technical incidents related to model performance, latency, response quality, or API errors. Optimize resource utilization (inference costs, token consumption, infrastructure scalability). Ensure rigorous technical monitoring, analyze logistical/technical regressions, and document solutions.

Customer Support and Enablement: Guide customer development teams and software architects on best practices for AI API integration, testing, security, and code robustness. Conduct technical workshops for scoping, user acceptance testing (UAT), and proof-of-concept (PoC) demonstrations. Communicate enhancement requirements and field feedback to the Product and R&D teams.

Desired Candidate Profile

Education and Experience: Education: Engineering degree (or equivalent Bac+5) in computer science, software engineering or artificial intelligence. Experience: 2-5 years of experience in a similar role (Advanced Support Engineer, Software Engineer, DevOps or Technical Consultant), combining a solid foundation in software development and a strong interest in the AI ​​ecosystem.

Technical Skills (Hard Skills): Development & SDLC: Mastery of the Software Development Life Cycle (SDLC), Agile/Scrum methodologies, code review practices, version control (Git), and unit/integration testing. Programming proficiency: Python (required), JavaScript/TypeScript or other back-end/front-end languages. AI & LLMs: Knowledge of AI frameworks and libraries (LangChain, LlamaIndex, Hugging Face, OpenAI API, Anthropic Claude, etc.) and vector databases. Cloud & DevOps: Experience with cloud environments (AWS, Azure, GCP), containerization (Docker, Kubernetes), and deployment automation (CI/CD). Architecture: Good understanding of REST APIs, GraphQL and service-oriented/microservices architectures.

Soft Skills: Customer Service Sense: Excellent listening, communication, and teaching skills to collaborate with development teams and simplify complex concepts. Analytical Mindset and Problem-Solving: Ability to investigate code, isolate, and quickly resolve cross-functional bugs (infrastructure, application code, and AI models). Autonomy and Adaptability: Ability to thrive in diverse and constantly evolving technical environments.