Join our cutting-edge generative AI (GenAI) platform, LIGER™, created by its technology studio, LMI Forge. LIGER™ harnesses the power of advanced technology, data analytics, and the latest in machine learning and natural language processing to provide secure, private, and trustworthy GenAI solutions for government.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.
Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.
A successful GenAI Project Lead will be highly skilled in project management, agile software delivery, communication, project planning, and financial management. The ideal candidate will be detail-oriented, possess the management skills to hold team members accountable to quality expectations and timelines, and respond swiftly to evolving client needs. A background in digital transformation and software development is highly desired. The GenAI Project Lead must demonstrate competency in leadership, strategic thinking, relationship management, multitasking, schedule management, and delivery excellence, while upholding the highest standard of ethical behavior.
Deeply understand the technical architecture of the tool, the tradeoffs between different algorithms and architectures, and explain these differences clearly to non-technical audiences
Understand what is and is not possible given our solution architecture and the models being used and help customers understand which of their problems can be solved with LLMs
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