Senior Engineer, AI Engineering (R5450)

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: The Senior Engineer, AI Engineering is a hands-on individual contributor responsible for building and operating AI-enabled solutions, reusable components, integrations, automations, and measurement capabilities that accelerate enterprise AI adoption. Reporting into the AI Engineering organization, this role works closely with the Staff Engineer, AI Platform & Architecture and the Director, AI Engineering to convert high-friction workflows into secure, reliable, measurable AI capabilities. The Senior Engineer delivers production-quality agents, prompts, connectors, dashboards, and workflow automations while following established architecture, governance, and cost-control standards. Success is defined by shipped capabilities that improve employee productivity, reusable components that reduce duplicate work, reliable telemetry that demonstrates impact, and strong collaboration with business and technology partners. What you'll do: AI Solution Delivery & Productivity Enablement • 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. Reusable Components & Integrations • 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. Responsible AI Controls & Operations • 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. Cost, ROI & Cross-Functional Execution • 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. Required qualifications: • 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 syst...