This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
The AI Solutions Engineer will play a crucial role in designing and deploying enterprise AI solutions, focusing on hands-on implementation. Candidates should have a strong background in Python and AI frameworks, with at least 5 years of experience. The role involves building AI applications, optimizing performance, and collaborating with senior engineers. This is a hybrid position with opportunities for growth in a leading technology company.
Job Responsibility:
Build and maintain AI applications including: RAG pipelines and knowledge assistants
LLM integrations and prompt workflows
Agentic AI bots
Develop APIs, backend services, and integrations supporting AI solutions
Assist in optimizing model performance, inference latency, and system reliability
Prepare datasets for AI use cases including cleaning, structuring, and preprocessing
Manage vector databases, embeddings, and retrieval optimization
Support automation of data ingestion workflows
Assist with deploying AI solutions across development, staging, and production environments
Monitor performance, troubleshoot issues, and optimize resource utilization
Support infrastructure setup (GPU inference environments, containers, cloud/on-prem deployments)
Work closely with solution architects and senior engineers
Support POC development, demos, and technical validation activities
Contribute to internal documentation and knowledge sharing
Requirements:
Minimum 5–10 years of relevant experience
Practical experience with: Python development (essential)
AI/ML frameworks or LLM integrations
APIs, backend development, or automation scripting
Familiarity with: Vector databases or semantic search concepts
Data processing and document parsing workflows
Containerization (Docker) or deployment environments
Understanding of Generative AI concepts (LLMs, embeddings, RAG basics)
Basic database knowledge (SQL/NoSQL)
Familiarity with Git-based development workflows
Nice to have:
Experience working on AI assistants, chatbots, or document AI projects
Exposure to GPU-based inference environments
Knowledge of cloud platforms or hybrid AI deployments
Experience with performance tuning or scaling AI systems