We are seeking an experienced Artificial Intelligence / Machine Learning Engineer to support the design, development, and implementation of advanced AI-driven solutions. This role requires strong hands-on experience building and deploying machine learning models, applying AI technologies to real-world problems, and collaborating with cross-functional teams to deliver scalable solutions.
The ideal candidate will have a strong technical background in AI development, machine learning, and data-driven application design, with the ability to quickly assess business needs and translate them into practical AI solutions.
Key Responsibilities
- Develop proof-of-concepts (POCs) and production-ready AI applications.
- Design, develop, and deploy AI and machine learning models to solve complex business problems.
- Build and optimize predictive models, NLP models, and data-driven algorithms.
- Collaborate with engineering, product, and data teams to integrate AI solutions into existing platforms and applications.
- Evaluate datasets, perform feature engineering, and prepare data for model training and testing.
- Monitor model performance and continuously improve accuracy and scalability.
- Document models, architecture, and implementation approaches for maintainability and scalability.
- Provide technical guidance on AI strategy, architecture, and best practices.
Required Qualifications
- Strong, demonstrable experience in Artificial Intelligence development and implementation.
- Proven machine learning experience with practical, real-world application.
- Experience building, training, and deploying machine learning models in production environments.
- Strong programming skills in Python, R, or similar languages.
- Experience with ML frameworks
- Experience working with large datasets and data pipelines.
- Knowledge of model evaluation, optimization, and performance tuning.
- Strong analytical and problem-solving skills.
Preferred Qualifications
- Experience with Natural Language Processing (NLP), Generative AI, or deep learning models.
- Experience integrating AI with cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with MLOps practices, model deployment pipelines, and containerization tools (Docker, Kubernetes).
- Experience building AI-powered applications, automation tools, or intelligent workflows.