Applied AI Engineer – Agents & Automation

The Frontier AI Deployed Engineer is a member of Future Electronics' AI & Automation Center of Excellence and works directly with business and technology teams to discover, design, build, and deploy AI-enabled solutions that transform how work gets done across the organization. This is a highly hands-on engineering role combining applied AI, agentic systems, AI-assisted software development, intelligent automation, enterprise integration, and business process transformation. Frontier Deployed Engineers move beyond experimentation by rapidly transforming ideas into working prototypes and evolving successful concepts into secure, scalable, and production-ready enterprise solutions. We are looking for AI-native engineers who use AI as a primary force multiplier throughout the engineering lifecycle. Successful candidates actively use tools such as Claude to accelerate research, discovery, solution design, prototyping, coding, testing, troubleshooting, documentation, and continuous improvement. The successful candidate is an exceptional communicator who is naturally curious, willing to explore unfamiliar technologies, and comfortable operating in ambiguous problem spaces. This individual knows how to collaborate effectively with AI while applying strong engineering judgment to validate AI-generated designs, code, tests, and recommendations. Key Responsibilities Discover & Transform • Partner directly with stakeholders across Supply Chain, Finance, HR, Sales Operations, Procurement, Customer Service, and other corporate functions to identify high-value opportunities for AI and automation. • Understand how work is performed, including workflows, decisions, data dependencies, pain points, exceptions, and operational constraints. • Use AI-assisted research and analysis to accelerate business discovery, identify patterns, and explore potential solutions. • Challenge existing processes and identify opportunities to fundamentally rethink how work can be performed using AI. • Facilitate discovery sessions and translate business challenges into actionable technical opportunities and measurable success criteria. • Prioritize opportunities based on business value, feasibility, scalability, data readiness, risk, and strategic alignment. • Use Claude and other AI tools to rapidly translate business problems into technical concepts, prototypes, and experiments. • Explore multiple AI-assisted approaches before committing to a solution architecture or technology. • Rapidly evaluate emerging AI tools, models, agent capabilities, development methods, and integration patterns. • Build working prototypes to validate business assumptions, technical feasibility, user value, and implementation risks. • Compare alternative approaches based on quality, security, maintainability, performance, cost, delivery speed, and scalability. • Demonstrate curiosity and a willingness to test new approaches rather than relying exclusively on established technologies or familiar patterns. • Share experiments, lessons learned, reusable components, and emerging best practices with the broader team. Build AI Solutions • Apply an AI-first engineering approach, using AI wherever it can materially accelerate solution discovery, architecture, development, testing, troubleshooting, documentation, and delivery. • Design, develop, test, and deploy AI-powered applications, intelligent agents, copilots, assistants, knowledge solutions, and decision-support tools. • Apply Generative AI, Large Language Models, Retrieval-Augmented Generation, vector search, document intelligence, natural language processing, tool calling, and related technologies to real business problems. • Design agentic workflows that connect AI capabilities, enterprise systems, automation platforms, and appropriate human oversight. • Use AI-assisted development to accelerate coding, test creation, code review, refactoring, debugging, technical documentation, and solution optimization. • Evaluate and optimize AI solutions for grounding, accuracy, reliability, security, performance, latency, consumption, and operating cost. • Validate AI-generated code, designs, tests, and recommendations before incorporating them into production solutions. • Apply strong software-engineering principles to transform successful experiments into maintainable and production-ready solutions. Deploy & Integrate • Integrate ERP, CRM, HRIS, supply chain, procurement, eCommerce, ticketing, collaboration, and internal systems through APIs and modern integration technologies. • Develop practical solution designs covering applications, AI services, data flows, integrations, identity, security, monitoring, and operational support. • Build reusable components, data pipelines, services, connectors, prompts, agent tools, and integration patterns that can scale across the organization. • Collaborate with Enterprise Architecture, Security, Data, Infrastructure, and Application teams to move solutions safely into production. • Apply appropriate source control, automated testing, CI/CD, observability, and operational-support practices. • Monitor deployed solutions and continuously improve their reliability, effectiveness, performance, cost, and user experience. Demonstrate AI-Enabled Delivery Acceleration • Demonstrate how AI-first engineering can reduce the time and effort required to move from a business problem to a working solution. • Compare AI-first and conventional development approaches when appropriate to identify differences in speed, effort, quality, maintainability, and cost. • Establish delivery baselines and track indicators such as time to first prototype, engineering effort, iteration cycles, defects, rework, time to production, and cost to deliver. • Document where AI materially accelerated delivery and where conventional engineering or additional human validation remained necessary. • Share practical examples and working demonstrations that help the organization understand how AI changes the solution-development lifecycle. • Identify repeatable AI-assisted engineering practices that can be adopted by other technology teams. Drive Adoption & Business Value • Work directly with users throughout discovery, prototyping, validation, deployment, adoption, and continuous improvement. • Communicate complex AI concepts, opportunities, limitations, risks, and trade-offs clearly to technical and non-technical audiences. • Support pilots, user-validation exercises, training, adoption, and feedback activities. • Establish business baselines and measure improvements in productivity, quality, cycle time, user adoption, cost, and other relevant outcomes. • Ensure technology deployment results in measurable improvements to how work is performed, rather than simply introducing a new technology. • Help business teams understand when AI is appropriate, when it requires human oversight, and when a conventional technology solution may be more suitable. Governance, Security & Responsible AI • Ensure solutions align with company security, privacy, compliance, data-governance, and change-management requirements. • Apply Responsible AI practices throughout solution design, development, testing, deployment, and monitoring. • Implement appropriate identity, authorization, logging, traceability, human oversight, and data-protection controls. • Identify and mitigate risks related to inaccurate AI output, prompt injection, inappropriate data exposure, excessive permissions, unintended automated actions, and insecure AI-generated code. • Ensure confidential or restricted company information is only processed using approved tools, models, environments, and access controls. • Maintain human accountability for AI-assisted engineering decisions and production releases. • Participate in security reviews, risk assessments, audits, and production-readiness activities. Qualifications Required • Bachelor's degree in Computer Science,