A/AI Research Engineer Stf - E4

Overview In this role you bridge AI research with practical, customer-focused demonstrations for defense and commercial customers. You own the AI demo environments and technical narratives that illustrate Panel’s capabilities across the sales cycle. You collaborate with cross-functional teams to translate complex ML/MLOps concepts into compelling value for executives and engineers. You shape prototypes and POCs, delivering repeatable, reliable demos at scale. You join a mission-driven team that combines cutting-edge AI with strategic customer engagement. Compensation / BenefitsMedical, Dental, VisionFlexible work arrangements401(k) matchPaid time offHolidaysParental Leave ResponsibilitiesBuild and maintain MLOps demo environments end-to-end (model lifecycle, versioning, CI/CD, deployment, monitoring) and ensure reliability across meetingsCreate industry-focused demo narratives and datasets mapping Panel capabilities to client workflowsDevelop reusable technical assets (reference architectures, ROI calculators, solution briefs, competitive sheets)Lead technical discovery with prospects, deliver customized demos to varied audiences, and support RFIs/RFPsEstablish trusted advisor relationships and hand off to Customer Success Engineers and Solution Architects after dealsMaintain expertise in Panel and general MLOps best practices; stay current with Kubeflow, MLflow, KServe, Ray, Argo, etc.Collaborate with Product/Engineering to feed customer feedback into the Panel roadmap and support partner enablement Key requirements5–9 years in Sales Engineering, Solutions Engineering, Pre-Sales Technical Consulting, or hands-on ML/MLOpsProficiency in Python and at least one ML framework (PyTorch, TensorFlow, scikit-learn, or equivalent)Proven ability to build and maintain end-to-end demo/POC environmentsstrong presentation and communication skillsability to build trusted advisor relationships with technical stakeholderscollaborative cross-functional mindsetMLOps platforms (Astris AI Factory, MLflow, Kubeflow, Weights & Biases, or similar)generative AI / LLM concepts (RAG, agent frameworks)Kubernetes and containerization