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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.

Check out Our brand video to learn more about us!

We are seeking a dynamic AI Engineer with proven expertise in deploying machine learning (ML) and deep learning (DL) models into production. You will play a key role in transforming AI models into production-ready applications, with a particular focus on LLM-native development, vector databases and building innovative AI-driven applications and services. In this role, you will collaborate with the data science team, driving experimentation, rapid iteration and the implementation of scalable AI solutions. A deep understanding of Python, cloud infrastructure (AWS, GCPorAzure) and experience with Large Language Models (LLMs) is essential.

Key Responsibilities

  • Deploy and optimize ML/DL models: Ensure scalability, performance and reliability in production environments, moving from experimental to production-grade solutions.
  • Develop LLM-native applications: Design, build and maintain AI applications utilizing large language models (LLMs), embracing an experimentation-driven process to unlock new capabilities.
  • 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.
  • Productization: Turn experimental models into production-ready applications by applying best practices in software engineering, cloud architecture and feedback mechanisms.
  • 3+ years of experience deploying AI models: Proven experience in putting ML/DL models into production environments at scale.
  • Expertise in building AI applications: Hands-on experience building AI-driven applications and services, with a focus on delivering practical solutions from experimental models.
  • 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.
  • Knowledge of caching strategies, cost optimization and debugging in LLM-based applications.

Acolad is committed to creating a diverse and equitable workforce. We believe that diversity, equity, and inclusion in all its forms - gender, age, disability, marital status, ethnic or social origin, religion, belief, or sexual orientation - enrich the workplace. It opens opportunities for individuals to express their talents, both individually and collectively, and strengthens our ability to adapt to a changing world. As an equal opportunity employer, we welcome and consider applications from all qualified candidates, regardless of their backgrounds.

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Average salary estimate

$125000 / YEARLY (est.)
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$100000K
$150000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About AI Engineer, Acolad

Join Acolad as an AI Engineer, where your expertise in deploying machine learning and deep learning models can truly shine! Acolad, the global leader in content and language solutions, is on the lookout for talent just like you to help drive growth and innovation across various industries. As an AI Engineer, you’ll be instrumental in transforming AI models into production-ready applications, especially with a focus on LLM-native development and vector databases. The ideal candidate will have at least 3 years of experience in deploying AI models at scale. You will collaborate closely with our data science team, driving experimentation and ensuring that our AI solutions not only meet but exceed performance expectations. Being proficient in Python and having hands-on experience with cloud platforms like AWS, GCP, or Azure is essential. Get ready to leverage your skills in an engaging and collaborative environment where continuous improvement and creativity are key pillars of our workplace culture. At Acolad, we believe that your success contributes to our success! So whether you're developing AI applications or working with MLOps best practices, you’ll find endless opportunities for growth in our diverse global community. Watch our brand video to discover more about our innovative work and vibrant team. We can’t wait for you to contribute to our mission!

Frequently Asked Questions (FAQs) for AI Engineer Role at Acolad
What are the key responsibilities of an AI Engineer at Acolad?

As an AI Engineer at Acolad, your responsibilities include deploying and optimizing ML/DL models for production, developing LLM-native applications, leveraging vector databases for improved performance, collaborating with data scientists, and implementing MLOps best practices.

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What qualifications are required for the AI Engineer position at Acolad?

To qualify for the AI Engineer position at Acolad, you need at least 3 years of experience in deploying AI models, a solid understanding of Python, familiarity with cloud platforms such as AWS, GCP, or Azure, and hands-on experience with Large Language Models (LLMs).

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How does Acolad support AI Engineers in their professional development?

Acolad supports AI Engineers through a collaborative global environment that promotes talent and creativity, offering continuous opportunities for learning and professional growth, as well as access to resources that aid in skill enhancement.

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What skills are preferred for an AI Engineer at Acolad?

Preferred skills for an AI Engineer at Acolad include familiarity with prompt engineering techniques, experience in creating Proofs of Concept (PoCs), knowledge of caching strategies, and a strong ability to design, experiment, and iterate with AI models.

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How important is MLOps knowledge for an AI Engineer at Acolad?

MLOps knowledge is crucial for an AI Engineer at Acolad as it ensures continuous integration and delivery of AI models, enhances performance monitoring, and optimizes the feedback loop, which is essential for successful AI solution development.

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Common Interview Questions for AI Engineer
Can you describe your experience with deploying machine learning models in production?

In your response, highlight specific projects you've worked on where you successfully deployed machine learning models, the challenges faced during deployment, and how you optimized the models for performance and reliability.

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What is your approach to developing LLM-native applications?

Discuss your methodology for building applications using large language models, including how you leverage experimentation and feedback to evolve your applications into robust solutions.

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How do you utilize vector databases in your AI projects?

Explain how you've previously integrated vector databases into AI workflows and the benefits it brought in terms of retrieval performance and application efficiency.

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What are your thoughts on the importance of collaboration in AI projects?

Share instances where collaboration with data scientists or product teams led to success in your projects, emphasizing effective communication and iteration as key elements.

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Can you give an example of a PoC you created and successfully transitioned to production?

Provide a detailed account of a specific PoC you developed, the steps taken to test its viability, and how you implemented it in a scalable, real-world application.

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What strategies do you use for monitoring and optimizing ML models?

Discuss the tools and techniques you've used for monitoring model performance and how you continually optimize models based on feedback and performance metrics.

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How familiar are you with prompt engineering techniques?

Share your experiences and expertise in prompt engineering, discussing any specific techniques like few-shot learning that you've implemented in your projects.

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What challenges have you faced when transitioning from experimental models to production?

Explain specific challenges you've encountered during this transition, including any technical issues and how you addressed them to ensure a smooth rollout.

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How do you stay updated on the latest trends in AI and machine learning?

Mention relevant resources such as industry blogs, academic papers, and professional organizations that you follow to keep your knowledge current and relevant.

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What do you believe is the future of AI applications?

Share your insights on the emerging trends in AI applications, how they may evolve, and what role you see yourself playing in this dynamic field.

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DATE POSTED
March 25, 2025

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