Duncan Aviation is seeking a highly driven Artificial Intelligence (AI) Engineer to support and grow our AI initiatives. If you are eager to take on meaningful challenges that help shape the future of AI where you work, we would love to have you on our team.

The Artificial Intelligence (AI) Engineer is responsible for designing, developing, and implementing cutting-edge AI and machine learning (ML) solutions to optimize business processes, drive innovation, and enhance end-user experiences. This role conducts extensive research and analysis of intelligent systems and develop AI/ML models and algorithms to deliver effective business solutions. The AI Engineer will work closely with a multidisciplinary team to identify, evaluate, and shape AI technologies for the organization.

Essential Job Functions

1. Collaborates with multidisciplinary teams, including project managers, data engineers/scientists, software engineers, business analysts, and business stakeholders, to understand and define the business problems that need to be solved using AI technologies and other mathematical techniques, while considering business objectives, requirements, and constraints.

2. Researches, prototypes, and conducts experiments to compare AI/ML tools, models, architectures, and algorithms. Uses independent judgement and research insights to select appropriate AI/ML models and algorithms based upon the business problem to be solved.

3. Collaborates with data engineers/scientists, domain experts, and team members to understand data sources and prepare datasets for AI/ML modeling including data cleaning, preprocessing, validation, and feature engineering.

4. Designs, builds, trains, and fine-tunes AI/ML models, which may include hyperparameter tuning, to solve business problems. Implements appropriate evaluation metrics to test, validate, and monitor AI/ML models and systems. Determines when to retrain and update AI/ML models based upon model drift, data growth, or requirement changes.

5. Works closely with team members, software engineers, and domain experts to integrate and deploy AI systems and models, including large language model-based systems such as retrieval-augmented generation or multi-agent systems, with production data, workflows, systems, and applications. Optimizes inference model pipelines for real-time performance and scale.

6. Creates clear and concise documentation of AI systems, models, algorithms, assumptions, and processes. Tracks key performance indicators, project progress, and results to provide regular reports and updates to project and business leadership. Communicates model architecture, decisions, behavior, and results to both technical and non-technical audiences.

7. Stays up to date with the latest AI techniques and tools, including algorithms, deep learning frameworks, large language models, and natural language processing techniques.

8. Collaborates with team members to create, administer, and maintain CI/CD and MLOps pipelines.

9. Ensures AI solutions adhere to ethical guidelines, data privacy regulations, and other legal requirements, while addressing potential bias, fairness, and explainability concerns.

10. Performs other duties as assigned.

Job Specific Requirements

• Licenses/Certificates: Valid driver’s license and acceptable driving record.
• Attendance: Regularly scheduled attendance is required
• Physical: Reads small print, repetitive motion, tolerates standing, walking, bending, kneeling, stooping, crouching, and sitting.
• Environmental: N/A

Education and Experience

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field required. A background in Machine Learning, Statistics, or Data Science is strongly preferred.
• Proficiency in Python required. Familiarity with additional programming languages such as Java or C++ beneficial.
• Knowledge of machine and deep learning concepts, including model architectures (neural networks, transformers, recurrent models, residual models, and autoencoders), model training types (supervised, unsupervised, and reinforcement learning), optimization techniques, evaluation techniques, and model deployment
• Experience working with machine learning algorithms and frameworks, such as TensorFlow, PyTorch, Scikit-learn or equivalent preferred.
• Experience with data processing and manipulation tools, such as NumPy, Pandas, SQL or equivalent preferred.
• Strong problem-solving and analytical skills required.
• Excellent communication and collaboration skills required.
• Ability to work independently and within a team environment required.
• Eagerness to learn, adapt, and grow in a fast-paced, dynamic environment required.