A/AI Research Engineer Stf - E4

Standard Job Description 
 Focuses on research & development of technologies that enable and advance semi and fully autonomous systems for both defense and commercial customers. Serves as the algorithm expert with up-to-date knowledge on modern AI research and may be involved in the inception of ideas and drive the development cycles from research to test of prototypes for a major project or component of a major project.Researches and discovers improvements to machine learning and robotic algorithms; Drives advancements in techniques used in signal processing, computer vision (CV), and control systems; Develops AI algorithms for mission systems; Applies latest research on AI algorithms and trains machine learning / deep learning models to solve a variety of problems; Investigates and applies the latest machine learning and deep learning techniques; Optimizes the performance of AI algorithms, applications, and platforms; Develops and documents algorithm and implementation requirements; Develops prototypes that will enable autonomous functionality in LM products and platforms; Interfaces with other teams involved the development lifecycle for perception, mission and motion planning, simulation and modeling, testing, etc. The Astris AI Sales Engineer serves as the technical backbone of our AI Factory go-to-market motion. This role owns the demo environments, proof-of-concept architectures, and technical narrative that show prospective enterprise customers what AI Factory platform — can do. It combines hands-on AI/ML engineering skill with strong presentation ability to support Account Executives across the full commercial sales cycle, from first technical discovery call through proof-of-concept and close. Demo & Technical Asset Development Build and maintain reusable MLOps demo environments on Panel covering the full model lifecycle: experiment tracking, versioning, CI/CD for ML, deployment, and production monitoring Develop industry-specific demo narratives and datasets (predictive maintenance, fraud detection, supply chain forecasting, and similar) that map Panel's capabilities to a prospect's actual workflows Maintain demo infrastructure, including containerized and Kubernetes-based environments, so demos run reliably across customer meetings, trade shows, and remote sessions Build reusable technical assets: reference architectures, ROI calculators, solution briefs, and competitive comparison sheets Customer Engagement Support Partner with Account Executives throughout the commercial sales cycle — qualification through close Lead technical discovery sessions to understand a prospect's existing infrastructure, data environment, team structure, and integration constraints Deliver customized demonstrations and technical presentations to audiences ranging from data scientists and ML engineers to CTO/CIO-level executives Respond to RFIs/RFPs with accurate technical content and MLOps-specific competitive positioning Build trusted-advisor relationships with customer technical stakeholders and support technical handoffs to Customer Success Engineers and Solution Architects once a deal closes Technical Enablement & Collaboration Maintain deep expertise in Panel and general MLOps best practices (model versioning, experiment tracking, CI/CD for ML, monitoring, governance) Stay current on the broader MLOps and Kubernetes ecosystem (Kubeflow, MLflow, KServe, Ray, Argo, and similar) to keep demos and competitive positioning sharp Collaborate with Product and Engineering to feed customer feedback and market signal into the Panel roadmap Support partner-channel enablement (ISVs, SIs, cloud partners) with MLOps-focused technical training and co-selling assets Basic Qualifications 
 5–9 years in Sales Engineering, Solutions Engineering, Pre-Sales Technical Consulting, or a hands-on ML engineering / MLOps role Working proficiency in Python and at least one ML framework (PyTorch, TensorFlow, scikit-learn, or similar) Demonstrated