# Senior Engineer, AI Engineering (R5450) **Company**: Shield AI **Work arrangement**: remote **Experience**: senior **Job type**: full-time **Salary**: USD 160,000-240,000 per-year-salary **Category**: Engineering **Industry**: Technology **Apply**: https://jobs.lever.co/shieldai/573fb642-d158-44dc-9310-3c293eb2eedc?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_3e39c724-856 ## 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. ## Responsibilities: ### 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. ## 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. ## Preferred Qualifications: - Experience in regulated, security-sensitive, defense-adjacent, or data-governed environments. - Familiarity with enterprise AI tooling ecosystems including copilot platforms, workflow automation suites, RAG platforms, vector databases, and enterprise search. - Experience with MLOps, model evaluation, AI observability, prompt/agent testing, or production monitoring. - Hands-on experience with data platforms such as Databricks, Snowflake, lakehouse architectures, or equivalent data infrastructure. - Experience developing usage dashboards, cost reporting, showback inputs, or ROI measurement for shared technology services. - Experience contributing to reusable component libraries, internal developer platforms, templates, or enablement playbooks. - Degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience. ## Skills ### Required - AI engineering - software engineering - API design - testing - observability - documentation - secure coding practices - AI governance - data classification - audit logging - prompt safety - output validation - model/prompt versioning - enterprise software - automation - data - digital workplace solutions - large language models - generative AI tools - APIs - RAG systems - agents - prompt workflows - AI-assisted automation ### Nice to have - MLOps - model evaluation - AI observability - prompt/agent testing - production monitoring - Databricks - Snowflake - lakehouse architectures - copilot platforms - workflow automation suites - RAG platforms - vector databases - enterprise search --- Source: [Apply at jobs.lever.co](https://jobs.lever.co/shieldai/573fb642-d158-44dc-9310-3c293eb2eedc?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)