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).
We’re partnering with a rapidly growing, innovation-focused organization that is making a significant investment in AI and advanced data capabilities in 2026. As part of this initiative, they are building out a net-new AI team (5–10 hires) to design and deliver cutting-edge, production-grade AI solutions. This is a high-impact opportunity to join early and help shape the architecture, frameworks, and best practices for AI across the business.
Job Responsibility
Design and build agentic AI systems leveraging large language models (LLMs)
Develop scalable AI solutions using modern data and AI platforms (Databricks preferred)
Translate business problems into production-ready AI workflows and applications
Collaborate with product and architecture teams to define AI use cases and technical approaches
Implement and optimize: Prompt engineering strategies
Token usage and cost efficiency
Model performance and response quality
Work with large-scale datasets to support training, fine-tuning, and inference workflows
Contribute to the development of AI engineering standards, frameworks, and best practices
Partner with data science teams to integrate models into production environments
Requirements
5+ years of experience in software engineering, data engineering, or AI/ML engineering
Hands-on experience building applications using LLMs (e.g., GPT, open-source models)
Experience with agentic AI frameworks (e.g., LangChain, AutoGen, CrewAI, or similar)
Strong programming skills in Python
Experience working with large datasets and distributed data platforms
Familiarity with Databricks or similar modern data platforms
Experience building production-grade AI systems (not just POCs)
Strong understanding of prompt engineering and LLM optimization techniques
Nice to have
Experience designing AI systems in enterprise environments
Background in both AI engineering and data science
Familiarity with model fine-tuning, embeddings, and vector databases
Experience with cloud platforms (AWS, Azure, or GCP)
Exposure to cost optimization strategies for LLM-based systems
Prior experience in highly regulated or data-intensive industries