AI (ML) Engineer - Colorado Springs, CO

Davidson Technologies•Colorado Springs, CO

4d•Onsite

About The Position

Davidson is seeking an AI (ML) Engineer to support an innovative Missile Defense Agency program developing modeling and simulation, optimization, and AI capabilities for the Guam Defense System. The role is part of a cross-functional team working in AI/ML, modeling and simulation, DevSecOps, and cybersecurity. The engineer will develop AI/ML products that inform the program’s AI/ML strategy, collaborate with delivery teams, and communicate concepts and results to technical and non-technical audiences. The position requires working with little direct supervision while contributing to a product team and meeting time-critical delivery schedules. The engineer will support multiple AI/ML projects and their development and training environment. Work includes collaborating with data team members and functional subject-matter experts, developing optimization models, training algorithms within software products, and supporting AI/ML projects through problem definition, data collection, development, deployment, and monitoring. The engineer will also work with software engineering, network security, and DevOps teams to deploy and improve production models, coordinate with teams using AI/ML products, and work with the AI/ML Lead to define problem statements and success metrics. The role is performed at a contractor facility in Colorado Springs, Colorado, and at Schriever SFB. Limited travel of less than 10% is required to support program meetings.

Requirements

Bachelor of Science degree in a STEM discipline and 4 to 6+ years of relevant experience, or a master's degree and 3 years of experience.
Fundamental understanding of AI/ML principles and experience supporting AI/ML projects from conception to deployment.
Familiarity with AI/ML techniques including deep learning, natural language processing, computer vision, and reinforcement learning.
Experience with Large Language Models/Generative AI and prompt engineering, including using LLM/GAI solutions for human-machine teaming in software development.
Understanding of heuristic and black-box optimization approaches, such as genetic algorithms, simulated annealing, Bayesian optimization, and particle swarm optimization.
Familiarity with ML algorithms such as decision trees, random forests, support vector machines, neural networks, convolutional neural networks, and long short-term memory networks.
Experience evaluating models using concepts such as cross-validation, drift monitoring, model cards, and bias/fairness testing.
Familiarity with responsible AI/model governance.
Proficiency building optimization models in at least one of Python, C, or C++.
Understanding of Python data tools including NumPy, Pandas, and SciPy.
Experience with Python ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
Experience with collaborative programming tools such as Git and GitLab.
Familiarity with Linux operating systems.
Strong communication, interpersonal, and presentation skills.
Strong problem-solving and analytical skills.
Self-motivated, able to work effectively in a dynamic, collaborative team, and able to work with little direct supervision.
Ability to travel occasionally, less than 10% of the time, for program meetings.
Active DoD Secret clearance at the time of application and ability to obtain a Top Secret clearance.

Nice To Haves

5+ years of relevant experience in Operations Research/Machine Learning.
Demonstrable experience building ML models in Python.
Experience with data manipulation and machine learning for big datasets.
Experience with MLOps tools such as Kubeflow, MLflow, or DVC.
Knowledge of modern application development, including containerization with Docker/Kubernetes.
Demonstrable experience applying analytical and programming skills to solve optimization problems.
Strong understanding of classical optimization techniques such as linear programming and gradient descent.
Familiarity with Agile processes and development methodologies.
Familiarity with cloud computing platforms such as AWS, Azure, or GCP.

Responsibilities

Serve as an AI Engineer on a team creating AI/ML projects and the environment used to develop and train them.
Collaborate closely with data team members and functional subject-matter experts.
Develop advanced optimization models to enhance customer products.
Train models and algorithms within software products in collaboration with a cross-functional team.
Support AI/ML projects throughout their lifecycle, from problem definition and data collection through model development, deployment, and monitoring.
Design, develop, and implement AI/ML models and algorithms using appropriate tools and techniques.
Test and validate AI/ML models to ensure quality and accuracy.
Collaborate with software engineers, network security, and DevOps teams to deploy models into production environments.
Monitor model performance in production and identify opportunities for improvement.
Communicate AI/ML concepts and results to technical and non-technical audiences.
Coordinate closely with multiple teams that use AI/ML products.
Work with the AI/ML Lead to define problem statements and success metrics.
Experiment with innovative theories, ideas, and concepts; participate in an Agile development lifecycle; build consensus across teams; and contribute design inputs and backlog refinement.