AI/ML Engineer III

Responsibilities

• Lead the design and development of advanced machine learning models, including deep neural networks, reinforcement learning systems, and generative AI algorithms, to solve complex problems.
• Architect scalable AI/ML systems that can integrate seamlessly with existing software and hardware platforms.
• Provide guidance on the selection of tools, frameworks, and infrastructure.
• Collaborate with systems engineers, hardware teams, and data scientists to align AI/ML solutions with mission-specific requirements and constraints.
• Manage AI/ML projects, including scoping, resource allocation, and timeline management, ensuring that deliverables meet quality and performance expectations.
• Develop and oversee robust validation and testing frameworks to ensure that AI/ML models meet performance, safety, and compliance standards in real-world scenarios.
• Mentor junior engineers, providing technical guidance and fostering a collaborative, innovative team environment.
• Stay current with emerging AI/ML technologies and propose innovative solutions to address new and existing challenges in the aerospace and defense domain.
• Communicate technical concepts, project progress, and outcomes to stakeholders, including leadership and external partners, in a clear and concise manner.
• Independently train, fine-tune, and optimize advanced AI architectures (including transformers) for complex applications.
• Apply a broad range of AI/ML techniques (supervised, unsupervised, reinforcement, generative) to solve domain-specific challenges.
• Design and execute large-scale simulations and modeling of AI/ML systems, ensuring scalability, performance, and robustness on CPU/GPU platforms.
• Lead rigorous validation and verification activities, ensuring deliverables meet performance, safety, and reliability requirements.

Qualifications

• You Must Have:
  • Bachelor’s degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline.
  • 6+ years of experience in a related field.
  • Relevant experience can be considered as a substitute for the required educational qualifications.
  • In the absence of a degree, a minimum of 9 years of related experience is required.
  • A higher level relevant degree may substitute for experience.
  • Proficient in machine learning frameworks (e.g., TensorFlow, PyTorch) and skilled in implementing advanced AI/ML techniques, such as supervised, unsupervised, and reinforcement learning (e.g., PPO, Actor/Critic), as well as working with generative AI models (e.g., transformers).
  • Proficient in developing, deploying, and optimizing AI/ML models, including ANNs, CNNs, and RNNs, for production applications.
  • Contributed to the design and scaling of systems for larger datasets or environments with moderate complexity.
  • Strong proficiency in programming languages such as Python, C++, C#, or Java, with experience in building scalable AI/ML systems.
  • Proven track record of deploying AI/ML models in production environments and optimizing them for real-world use cases.
  • Demonstrated experience leading teams or projects, including mentoring junior staff.
  • Knowledge of regulatory and cybersecurity requirements for AI/ML systems in aerospace and defense applications.
• We Prefer:
  • A Master’s degree in Artificial Intelligence, Machine Learning, or related field.
  • Experience leading teams or projects in aerospace and defense industries.
  • Familiarity with cybersecurity and regulatory requirements.
  • Practical experience designing and implementing advanced ML techniques, such as clustering, dimensionality reduction, or generative modeling.
  • Hands-on experience with GPU programming and using high-performance computing systems for ML workloads.
  • Experience with at least one reinforcement learning or generative AI model (e.g., implementing GANs or Transformers).
  • Able to analyze large-scale, heterogeneous datasets and apply advanced statistical and ML methods to real-world problems.
  • Experience translating mission objectives into actionable system requirements, including HMI scenarios.
  • Familiarity with hardware acceleration (e.g., CUDA, TensorRT) and introductory knowledge of edge AI or XAI.