The Opportunity
Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.
Join our team as the expert you are now and create your future.
Position Summary
We are looking for an experienced AI Engineer to join our forward-thinking Emerging Technology Practice. In this role, you will play a vital part in developing, training, and deploying a range of machine learning models, including traditional statistical models, deep learning, and cutting-edge large language models (LLMs). You will also fine-tune pre-trained models through transfer learning and develop robust MLOps pipelines to ensure seamless integration and operation of AI solutions.
You will be responsible for driving the AI/ML strategy and working closely with engineers and business stakeholders to deliver scalable, high-performance solutions. Your role will extend from data preprocessing and feature engineering through to model development, optimization, deployment, and monitoring. You will also collaborate with cross-functional teams to integrate AI models into production environments and continuously improve model performance.
This position offers an opportunity to work on groundbreaking AI/ML projects that impact industries like healthcare, higher education, and finance. If you’re passionate about AI and ML, thrive in a collaborative environment, and want to make a tangible impact through technology, this is the perfect role for you.
Key Responsibilities
- Develop and train traditional statistical ML models and deep learning models to address real-world problems in healthcare, education, and other sectors.
- Fine-tune large language models (LLMs) using transfer learning techniques to optimize performance for specific tasks.
- Design, implement, and maintain MLOps pipelines to streamline the lifecycle of ML models, from development through to deployment and monitoring in production.
- Conduct data preprocessing, feature extraction, and data augmentation to ensure the quality of training datasets.
- Deploy models into production environments, ensuring seamless integration with existing systems.
- Optimize model performance by experimenting with hyperparameters, architectures, and model updates to meet performance benchmarks.
- Develop scalable APIs and services for model serving and inference in production.
- Establish monitoring systems to track the performance, accuracy, and data drift of deployed models.
- Collaborate with cross-functional teams to integrate AI/ML solutions into broader technology stacks.
- Stay updated on the latest advancements in machine learning, natural language processing, and MLOps best practices.
Candidate Profile
You are a highly technical AI Engineer with experience in developing, training, and deploying a wide variety of machine learning models. You possess strong knowledge in statistical models, transfer learning, and hands-on experience with MLOps to automate and optimize the AI development lifecycle.
Key Qualifications
- Minimum of 7 years' experience working with machine learning models, including both traditional statistical methods and deep learning techniques.
- Proven experience in training and fine-tuning large language models (LLMs) and implementing transfer learning strategies.
- Expertise in developing and deploying AI models using Python and frameworks such as TensorFlow, PyTorch, scikit-learn, or similar.
- Strong understanding of MLOps principles, with experience in building automated pipelines for model training, deployment, and monitoring.
- Familiarity with cloud-based ML platforms such as Azure Machine Learning, AWS SageMaker, or Google AI/ML services.
- Knowledge of data preprocessing techniques, feature engineering, and model evaluation metrics.
- Experience with version control (Git), containerization (Docker), and CI/CD tools for model deployment.
- Ability to write and deploy scalable APIs and model serving frameworks.
- Strong communication skills, with the ability to work effectively in a collaborative team environment.
- Cloud certifications in AI/ML services (Azure, AWS, or Google Cloud) are a plus.
- Bachelor's degree in computer science, engineering, or a related field, with a focus on AI or machine learning.
- Authorized to work in the US or Canada (No sponsorship required).
