AI Innovation Engineer

About

At Exponential AI, we are a pioneering force in applying artificial intelligence to solve complex, real‑world challenges across industries. As an AI‑driven company, we develop innovative, scalable solutions that empower businesses to make intelligent, real‑time decisions. We are currently seeking a highly skilled and innovative AI Innovation Engineer to join our Legal Innovation and Strategy department. In this role, you will have the opportunity to design and implement advanced machine learning (ML) models, working with diverse datasets and applying AI techniques to create impactful solutions that address critical business problems.

If you have a passion for AI, machine learning, and solving complex problems in a dynamic and fast‑paced environment, this is the perfect opportunity for you to grow and make a meaningful impact.

Responsibilities

• Assist in the implementation of AI‑powered services both in cloud and on‑premise environments.
• Solve business and research problems through advanced techniques such as natural language processing (NLP), image recognition, and text recognition.
• Analyze large and complex datasets to uncover insights, relationships, and inconsistencies.
• Stay updated with the latest trends in AI and ML, incorporating new methodologies into system designs.
• Implement metrics and processes to track and improve the performance of ML models.
• Collaborate with data engineers, architects, and other cross‑functional teams to enhance data quality and system functionality.
• Develop frameworks to evaluate and assess commercial AI solutions and their potential applications.
• Provide training on AI tools and complex concepts to stakeholders across various teams.

Requirements

• Bachelor’s degree in Data/Computer Science, Statistics, or Business Analytics (or equivalent combination of education and experience).
• Certifications in Microsoft Azure, AI, or Data Science (e.g., Applied Data Science) are highly desirable.
• Proven experience with predictive and automation tools.
• Hands‑on experience with large‑scale data repositories and cloud infrastructures (preferably Azure).
• Experience in designing, deploying, and scaling ML solutions across different industries.
• Strong understanding of Large Language Models (LLMs) and their practical applications.
• Proficiency in building and optimizing ML models using frameworks like TensorFlow, PyTorch, or Keras.
• Experience with DevOps tools (e.g., GIT, Azure DevOps) and Agile development processes.
• Expertise in AI concepts and techniques, with the ability to communicate technical insights to non‑technical stakeholders.
• Proficient in programming languages such as Python, R, SQL, and familiar with data visualization tools like Power BI or Tableau.
• Experience with cloud platforms such as Azure, and machine learning libraries like scikit‑learn.
• Strong knowledge of database design, data mining, and AI model implementation.
• Experience in deploying end‑to‑end machine learning workflows and models at scale.