Ai Engineer

**Acolad is the global leader in content and language solutions. Its mission is to support companies in every industry to scale across markets and enable growth through cutting-edge technology and localization expertise. Established in 1993, the group is present in 23 countries across Europe, North America, and Asia, with over 1.800 employees supported by a network of +20.000 linguists around the world.**

**At Acolad, every position is key to our global growth: we know that we will only succeed if our people succeed.**

**Joining Acolad means a unique opportunity for professional development through a collaborative global environment that promotes talent and creativity. We are continuously looking for new talent (like you) to support our mission to drive growth and innovation across some of the world’s leading brands.**

**Key Responsibilities**
- Deploy and optimize ML/DL models: Ensure scalability, performance and reliability in production environments, moving from experimental to production-grade solutions.
- Leverage vector databases: Utilize vector databases to enhance retrieval performance and streamline AI workflows.
- Collaborate and iterate: Work closely with data scientists and product teams to integrate models and solutions into business operations, rapidly iterating through experimentation and proof-of-concepts (PoCs).
- Implement MLOps best practices: Ensure the continuous integration and delivery of AI models through MLOps pipelines, incorporating monitoring and feedback loops.

**Requirements**:

- 3+ years of experience deploying AI models: Proven experience in putting ML/DL models into production environments at scale.
- Experience with Large Language Models (LLMs): Strong understanding of LLMs, including their experimentation and productionization.
- Vector databases: Experience with vector databases for enhanced retrieval and AI performance.
- Proficiency in Python and cloud platforms: Deep expertise in Python and experience with cloud platforms such as AWS, GCP, or Azure.
- Research and experimentation mindset: Ability to design, experiment and iterate with LLMs and AI models, balancing lean experimentation and structured workflows.
- MLOps knowledge: Familiarity with MLOps pipelines, monitoring and optimization techniques for continuous delivery and feedback integration.
- Problem-solving and adaptability: Ability to navigate a fast-paced environment, embracing agility and continuous improvement in AI solution development.

**Preferred Skills**:

- Familiarity with prompt engineering techniques, such as few-shot learning and dynamic prompt optimization.
- Experience in creating PoCs and transitioning them into scalable, production-grade AI systems.