AI Digital Systems Engineer

MTSI is seeking an AI Digital Systems Engineer to design, develop, and integrate artificial intelligence capabilities into modern digital systems. This role focuses on bridging AI/ML technologies with software, data, and system architectures to enable intelligent decision‑making, automation, and advanced analytics within complex engineering environments. The ideal candidate combines systems engineering discipline with strong AI and software development skills
, and is comfortable working across the full lifecycle from concept and architecture through deployment and operational support.

• Design and develop AI‑enabled digital systems, including data pipelines, machine learning models, and software integrations.
• Architect and implement end‑to‑end AI solutions, from data ingestion and preprocessing to model deployment and monitoring.
• Integrate AI capabilities into existing digital platforms, simulation environments, and enterprise systems.
• Collaborate with systems engineers, software developers, data scientists, and domain experts to translate operational needs into AI‑driven solutions.
• Develop and maintain digital architectures that support scalable AI and analytics capabilities.
• Evaluate emerging AI technologies, tools, and frameworks to improve system performance and innovation.
• Implement model validation, testing, and performance monitoring to ensure reliability and accuracy.
• Support Dev Ops practices, including automated pipelines, containerization, and continuous integration.
• Document system architectures, design decisions, and engineering processes.
• Ensure solutions align with security, data governance, and regulatory requirements.

• Bachelor’s degree in Computer Science, Software Engineering, Systems Engineering, Electrical Engineering, or related field.
• 3+ years of experience in software engineering, AI/ML development, or digital systems engineering.
• Strong programming skills in Python, Java, C++, or similar languages.
• Experience developing or integrating machine learning models.
• Familiarity with data engineering concepts including data pipelines, ETL processes, and structured/unstructured data.
• Experience with cloud or distributed computing environments (AWS, Azure, or similar).
• Knowledge of software development lifecycle and systems engineering practices.
• Strong analytical, problem‑solving, and communication skills.