AI Software & System Architect Sr - DevSecOps (on-site, Moorestown NJ)

Lockheed Martin•Moorestown, NJ

38d•$104,900 - $184,920•Onsite

About The Position

Are you ready to join a team that's pushing the boundaries of innovation and making a real impact in the field of artificial intelligence? The Lockheed Martin Artificial Intelligence Center (LAIC) team is seeking a highly motivated and skilled individual to support the Applied AI team and radar track processing product portfolio, DevSecOps, software, data science, data at scale, artificial intelligence, and are passionate about working on a broad range of projects that can make a real difference, we want to hear from you. What you'll be doing: • Leading the monitoring and maintenance of fielded AI capabilities and developing new capabilities to enhance ML monitoring and continuous update processes for deployed systems • Analyzing customer data and assessing ML model performance to identify areas for improvement • Developing and updating ML models to address evolving operational requirements • Collaborating with cross-functional teams to rapidly iterate and mature solutions • Providing continuous maintenance for deployed products using machine learning operations paradigms Who are you: You are a highly motivated and skilled individual with: • A strong working knowledge of radar systems engineering, signal processing, DevSecOps, software, data science, data at scale, artificial intelligence, and machine learning • Excellent self-motivation, work ethic, time management, and interpersonal skills The ability to work effectively in a team environment and collaborate with cross-functional teams • A strong understanding of machine learning operations paradigms and experience with continuous maintenance of deployed products • The ability to obtain and maintain a security clearance • A US Citizenship requirement due to the nature of the work • Ability to work on site at LM Moorestown between 50% and full time, with occasional off-hours work required to meet mission requirements #LMLAIC

Requirements

Bachelor's Degree in Engineering, Computer Science, or other related discipline
Skilled in training and analysis steps in a modern MLOps pipeline including labeling, local and global explainability, model export and serialization, conformance testing, deployment, and monitoring
Experience with DevOps tools: Docker, Git [GitLab, GitHub], Continuous Integration [CI], Continuous Deployment [CD]
Skilled in Python and multiple libraries (NumPy, SciPy, Pandas, etc.)
Ability to obtain a Secret government security clearance
A strong working knowledge of radar systems engineering, signal processing, DevSecOps, software, data science, data at scale, artificial intelligence, and machine learning
Excellent self-motivation, work ethic, time management, and interpersonal skills The ability to work effectively in a team environment and collaborate with cross-functional teams
A strong understanding of machine learning operations paradigms and experience with continuous maintenance of deployed products
The ability to obtain and maintain a security clearance
A US Citizenship requirement due to the nature of the work
Ability to work on site at LM Moorestown between 50% and full time, with occasional off-hours work required to meet mission requirements

Nice To Haves

Master's Degree in Engineering, Computer Science, or other related discipline
Skilled in leading long-term programs related to software and machine learning
Experience interfacing with databases (SQL, MongoDB, etc.)
Experience with machine learning model training and analysis through open-source frameworks (Pytorch, Tensorflow, Sklearn, Keras, etc)
Experience with Rust, C++, and other compiled languages
Experience with test events or integration activities
Experience coordinating long term program execution across organizations
Skilled in serving as an external spokesperson on significant technical matters related to software, machine learning, and radar systems

Responsibilities

Leading the monitoring and maintenance of fielded AI capabilities and developing new capabilities to enhance ML monitoring and continuous update processes for deployed systems
Analyzing customer data and assessing ML model performance to identify areas for improvement
Developing and updating ML models to address evolving operational requirements
Collaborating with cross-functional teams to rapidly iterate and mature solutions
Providing continuous maintenance for deployed products using machine learning operations paradigms