Senior Engineer, AI Engineering

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Shield AI

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2015

🤖 Artificial Intelligence

🚀 Aerospace

🎖️ Defense

Artificial Intelligence • Aerospace • Defense

Shield AI is a leading developer of AI-driven military solutions, focusing on enhancing mission autonomy and battlefield awareness. Their platform, Hivemind, enables rapid deployment of intelligent systems for various defense applications, including drone operation and surveillance. With a commitment to utilizing advanced technology, Shield AI aims to protect service members and civilians by revolutionizing defense technologies through autonomous systems.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

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