AI Lead Developer

Rigil Corporation•Atlantic City, NJ

1d•Onsite

About The Position

Rigil is an award-winning, woman-owned, small business that specializes in technology consulting, strategy consulting and product development. We value teamwork and strive to build strong leaders. This position will support a matrix team of talented personnel to conduct research development and test and evaluation of the safe integration of emerging operations. This work will be accomplished using the unique laboratory capabilities and tools resident at the customers for Advanced Aerospace, co-located at the Atlantic City. The team has been tasked to lead Human in the loop (HITL) simulations to inform initial entry into service at select major metropolitan areas and the team is researching future states for AAM operations as described in the customer’s Urban Air Mobility Concept of Operations. The team intends to use AI, rule-based algorithms, and large language modeling to create software with the capability to evaluate vertiport placements and potential airspace usage (routes, corridors). This sub-task will enable pre-evaluation of potential vertiport sites and proposed routes to support suitability decisions earlier in an agile, swift M&S process for this applied research. Also, more routes and sites will be evaluated faster as precursor to the HITL simulations, saving both time and money.

Requirements

Degree Requirement: At minimum, a Bachelor of Science degree in Engineering, Math, or Science from an accredited college or university.
At least eight (8) years of relevant experience.
Must be a U.S. citizen or qualified to work for a U.S. government agency.

Responsibilities

Supporting software development of an AI rule-based algorithms, and large language modeling with the capability to evaluate vertiport placements and potential airspace usage (routes, corridors).
Participating in agile planning discussions including Sprint Planning (what to deliver), Daily Stand-ups a.k.a “Scrums” (progress/blockers), Reviews (feedback), and Retrospectives. These meetings, often using Story Points for estimation, ensure a sustainable, realistic pace.
Designing and implementing data ingestion, cleaning, and structuring workflows to support repeatable analysis of simulation outputs.
Developing machine learning models to generalize and extrapolate findings from one simulated airport or vertiport environment to comparable operational environments.
Conducting predictive analytics and sensitivity analyses to assess throughput, safety margins, infrastructure impacts, and operational tradeoffs.
Supporting the building of automated analytics pipelines and scenario comparison tools to support rapid iteration across airport configurations and traffic assumptions.
Providing and ensuring model transparency, traceability, and explainability to support engineering validation.
Developing data visualizations and decision-support products that translate complex simulation results into actionable insights for scientists, engineers, researchers, and leadership.
Supporting integration of AI-enabled analytics with existing customer simulation environments and research workflows