AI Deployment Engineer

Job Summary

GE Aerospace is seeking a highly skilled AI Deployment Engineer to design, develop, and deploy advanced AI/ML solutions that transform commercial engine services operations. In this role, you will be an integral part of a data science or cross-disciplinary team working on commercially facing development projects, often involving large, complex data sets. These teams include statisticians, computer scientists, software developers, engineers, product managers, and end users, collaborating with partners in GE business units. Potential application areas span remote monitoring and diagnostics across industrial sectors, financial risk assessment, and operations optimization.

Roles and Responsibilities

• Design and deploy AI models utilizing physics-based analytics, deep learning, and LLMs to support predictive maintenance, fleet optimization, and executive decision-making, impacting billions in cost savings.
• Collaborate closely with software developers and software engineers to translate complex algorithms into commercially viable products and services.
• Work within technical teams on the development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
• Perform comprehensive exploratory and targeted data analyses using descriptive statistics and other advanced methods.
• Partner with data engineers on critical data quality assessment, data cleansing, and overall data analytics initiatives.
• Generate detailed reports, annotated code, and other project artifacts to thoroughly document, archive, and effectively communicate your work and outcomes.
• Share and actively discuss findings and insights with team members to foster collaborative problem-solving.

Required Qualifications

• Bachelor's Degree in Computer Science or a STEM Major (Science, Technology, Engineering, and Math).
• Minimum of 2 years of experience in digital technology.
• Legal authorization to work in the U.S. is required. GE Aerospace will not sponsor individuals for employment visas, now or in the future, for this job opening.
• Ability and willingness to travel domestically and internationally approximately 25-35% per year.

Desired Characteristics

• 2+ years of hands-on experience deploying and maintaining machine learning models on cloud platforms (AWS, Azure, Google Cloud).
• 2+ years of experience training and implementing analytics solutions using physics-based approaches, deep learning algorithms, and/or LLMs (aerospace domain experience is preferred but not required).
• Demonstrated ability to quickly learn and adapt to new domains, including commercial engine services, durability modeling, in-service maintenance, and long-term revenue accounting.
• Proven experience collaborating effectively with subject matter experts across diverse domains, including data science, fleet management, and digital technology.
• Strong problem-solving skills and the ability to translate complex business requirements into scalable technical solutions.
• Experience working in enterprise environments with large-scale data and mission-critical applications is preferred.

Core Technical Skills

• Proficiency in Python, ML/AI frameworks (PyTorch, TensorFlow), and cloud platforms (AWS, Azure, Google Cloud) for model deployment.
• Expertise in ML/AI DevOps, including containerization, CI/CD pipelines, model lifecycle management, and production monitoring.
• Experience with LLM fine-tuning, deep learning algorithms, and the integration of physics-based approaches with data-driven methods.
• Strong foundation in machine learning, statistical modeling, and working with large-scale enterprise datasets.
• Ability to rapidly learn new domains and collaborate effectively with subject matter experts across diverse technical areas.

Leadership

• Demonstrated awareness of how to function effectively in a team setting.
• Demonstrated awareness of critical thinking and robust problem-solving methods.
• Demonstrated awareness of strong presentation skills.

Personal Attributes

• Demonstrated awareness of how to leverage curiosity and creativity to drive significant business impact.

Key skills/competency

• AI Deployment
• Machine Learning Operations (MLOps)
• Cloud Platforms (AWS, Azure, GCP)
• Deep Learning
• Large Language Models (LLMs)
• Python
• Predictive Maintenance
• Fleet Optimization
• Data Analysis
• CI/CD Pipelines