AI Assurance Research Engineer

Date Posted:

2024-12-17

Country:

United States of America

Location:

UT13: RC-CT - Corp 411 Silver Lane, East Hartford, CT, 06108 USA

Position Role Type:

Hybrid

RTX Corporation is an Aerospace and Defense company that provides advanced systems and services for commercial, military and government customers worldwide. It comprises three industry-leading businesses – Collins Aerospace Systems, Pratt & Whitney, and Raytheon. Its 185,000 employees enable the company to operate at the edge of known science as they imagine and deliver solutions that push the boundaries in quantum physics, electric propulsion, directed energy, hypersonics, avionics and cybersecurity. The company, formed in 2020 through the combination of Raytheon Company and the United Technologies Corporation aerospace businesses, is headquartered in Arlington, VA. 

   

To realize our full potential, RTX is committed to creating a company where all employees are respected, valued and supported in the pursuit of their goals. We know companies that embrace diversity in all its forms not only deliver stronger business results, but also become a force for good, fueling stronger business performance and greater opportunity for employees, partners, investors and communities to succeed.  

The following position is to join our RTX Research Center team: 

Job Description

The AI Systems Engineering team researches and develops solutions using model-based systems engineering, formal methods, planning, decision making, controls, machine learning, anomaly detection, computer vision, and failure analysis techniques for a variety of high impact real world problems in the aerospace, manufacturing, and defense industries.

We are looking for an AI Systems Engineer to help develop, deploy, monitor, and maintain ML/AI-based platforms and tools relevant to safety-critical AI-enabled systems.

You will get to develop state-of-art AI Systems tools and apply them to real world datasets at scale. Interact and learn from leading researchers on a variety of topics working at RTRC. Support for publishing results in top conferences. You will work with a small and focused team on implementing AI-based systems engineering tools for deployment of AI in safety-critical AI-enabled systems. You will get to work on challenging problems within the broad areas of AI/ML applications in aerospace and defense.

What You Will Do:

The AI Systems Engineer will be responsible for one or more of the following activities:

• Leverage your strong software engineering expertise and experience to develop robust solutions for real-world problems

• Algorithm development, software implementation, performance profiling and optimization for various kinds of AI/ML workloads

• AI Platform Development: Assist in design and development of AI/machine learning-based platforms for various safety-critical technical problems

• AI/ML Model Deployment: Assist in deploying AI/machine learning models into production environments

• AI/ML pipeline and model monitoring: Contribute to code development and tool rollout for monitoring and maintenance of model performance and infrastructure health

• Automation: Participate in the development and maintenance of automated AI systems pipelines

• Collaboration: Under guidance from project leads, collaborate with the domain experts to integrate machine learning models into production systems

• Problem Solving: Assist in troubleshooting and resolving AI platform-related issues

• Research and stay updated on the latest trends and technologies in DevOps, MLOps, and AIOps

Qualifications You Must Have:

• Typically requires: A University Degree or equivalent experience and minimum 10 years prior relevant experience, or An Advanced Degree in a related field and minimum 7 years experience Engineering/Other Technical Positions: Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 10 years of prior relevant experience

• Proficient in one or more programming languages, such as Python, Java, C++

• Proficient in working with AI frameworks and tools, such as TensorFlow, PyTorch, Scikit-learn

• Experience developing, training, and debugging ML pipelines for computer vision, natural language processing (NLP)

• Experience in working with cloud platforms, especially AWS and Azure

• Experience in working with CI/CD frameworks such as GitLab, Jenkins

• Experience in working with container and workload management frameworks such as Docker, Kubernetes, Airflow

• Experience in infrastructure as code technologies such as CloudFormation, Terra