Senior Engineer, AI Engineering

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Shield AI

WebsiteLinkedInAlle Stellen

501 - 1000 Mitarbeiter

Gegründet 2015

🤖 Künstliche Intelligenz

🚀 Luft- und Raumfahrt

🎖️ Verteidigung

Artificial Intelligence • Aerospace • Defense

Shield AI ist ein führender Entwickler von KI-gesteuerten militärischen Lösungen mit dem Schwerpunkt auf der Verbesserung der Missionsautonomie und des Situationsbewusstseins auf dem Schlachtfeld. Ihre Plattform, Hivemind, ermöglicht die schnelle Bereitstellung intelligenter Systeme für verschiedene Verteidigungsanwendungen, einschließlich Drohnenbetrieb und Überwachung. Mit dem Engagement für den Einsatz fortschrittlicher Technologien zielt Shield AI darauf ab, durch autonome Systeme Verteidigungstechnologien zu revolutionieren und so Dienstmitglieder und Zivilisten zu schützen.

Beschreibung

• Build AI-assisted tools, workflow automations, agents, prompts, and integrations that reduce manual effort and improve individual and team productivity. • Partner with business stakeholders to understand high-friction workflows, translate them into technical requirements, and deliver fit-for-purpose AI solutions. • Implement AI-augmented collaboration patterns such as meeting intelligence, document generation, contextual knowledge retrieval, task automation, and internal assistant workflows. • Develop and maintain internal enablement assets including prompt templates, agent examples, skill templates, playbooks, and usage guidance. • Collect user feedback and operational telemetry to improve adoption, usability, reliability, and measured impact. • Build and maintain reusable AI components including connectors, integration adapters, prompt modules, data pipelines, skill templates, and service wrappers. • Contribute to shared component libraries using established quality, documentation, versioning, testing, and deprecation practices. • Integrate AI capabilities with enterprise systems, collaboration tools, knowledge repositories, data platforms, and workflow automation platforms. • Create developer-facing documentation, examples, and onboarding material that help other teams adopt shared AI components safely and efficiently. • Identify repeatable patterns from project work and convert them into reusable assets for broader enterprise use. • Implement engineering controls for data handling, access management, prompt safety, output validation, audit logging, and secure integration patterns. • Follow enterprise AI architecture and governance standards while escalating gaps, risks, or implementation challenges to technical leads. • Build or maintain dashboards for AI usage, adoption, policy adherence, cost visibility, error patterns, and operational health. • Support model, prompt, and agent lifecycle activities such as evaluation, version tracking, testing, rollout, monitoring, and rollback. • Participate in security, privacy, and governance reviews by providing implementation details, evidence, and remediation support. • Instrument AI solutions to capture usage, performance, cost, quality, and productivity metrics. • Support cost optimization work through usage analysis, model efficiency improvements, license rationalization inputs, and service tuning. • Help connect AI solution usage to measurable outcomes such as time savings, error reduction, throughput improvement, and capacity creation. • Collaborate with Engineering, IT, Security, Legal, Data, Finance, and business unit teams to deliver reliable AI capabilities in a matrixed environment. • Contribute to AI communities of practice by sharing lessons learned, reusable patterns, demos, and implementation guidance.

🎯 Anforderungen

• Progressive experience building enterprise software, automation, data, AI, or digital workplace solutions. • Hands-on experience integrating large language models, generative AI tools, APIs, RAG systems, agents, prompt workflows, or AI-assisted automation into production or enterprise environments. • Strong software engineering fundamentals including API design, testing, observability, documentation, secure coding practices, and maintainable implementation patterns. • Experience building integrations with enterprise systems, collaboration platforms, knowledge repositories, data platforms, or workflow automation tools. • Working knowledge of AI governance concepts such as access controls, data classification, audit logging, prompt safety, output validation, and model/prompt versioning. • Ability to convert ambiguous business workflows into practical technical solutions in partnership with stakeholders. • Experience instrumenting systems with telemetry, logging, dashboards, usage metrics, or cost/performance monitoring. • Clear communication skills and a collaborative style suitable for working across business, engineering, security, legal, and data teams.

🏖️ Vorteile

• Pay within range listed + Bonus + Benefits + Equity • Temporary benefits package (applicable after 60 days of employment)