AI Deployment Engineer- Fleet Management
- Design and deploy AI models using physics-based analytics, deep learning, and LLMs for supporting predictive maintenance, fleet optimization, and executive decision-making with billion-dollar cost impacts
- Work alongside software developers and software engineers to translate algorithms into commercially viable products and services.
- Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
- Perform exploratory and targeted data analyses using descriptive statistics and other methods.
- Work with data engineers on data quality assessment, data cleansing and data analytics
- Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes.
- Share and discuss findings with team members.
- Bachelor's Degree in Computer Science or "STEM" Majors (Science, Technology, Engineering and Math)
- Minimum of 2 years' experience in digital technology
- Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening.
- Ability and willingness to travel domestically and internationally around 25-35% per year.
- 2+ years of hands-on experience deploying and maintaining machine learning models in 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 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 ability to translate complex business requirements into scalable technical solutions
- Experience working in enterprise environments with large-scale data and mission-critical applications preferred
- 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 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
- Demonstrated awareness of how to function in a team setting
- Demonstrated awareness of critical thinking and problem-solving methods
- Demonstrated awareness of presentation skills
- Demonstrated awareness of how to leverage curiosity and creativity to drive business impact
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