Frontier AI Deployed Engineer

Position Summary

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.

Explore & Innovate

• 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, Software Engineering, Information Systems, Artificial Intelligence, Data Science, or a related field. Equivalent practical experience will also be considered.
• Minimum five years of experience in software engineering, solution development, systems integration, automation, data engineering, or a related technical field.
• Hands-on experience using Claude for AI-assisted engineering activities such as research, solution design, prototyping, software development, testing, troubleshooting, analysis, or technical documentation.
• Demonstrated ability to use AI tools to accelerate delivery while independently validating generated designs, code, tests, and technical recommendations.
• Demonstrated hands-on experience applying Generative AI, AI copilots, intelligent agents, LLMs, or related technologies to real-world business or technical challenges.
• Experience taking business-critical technology solutions from concept or prototype into production.
• Strong programming skills using Python, C#, JavaScript, TypeScript, or comparable modern development languages.
• Strong understanding of REST APIs, web services, databases, cloud application development, source control, automated testing, CI/CD, and modern software-engineering practices.
• Ability to evaluate when an AI-first development approach is appropriate and when conventional engineering methods remain necessary.
• Strong analytical and problem-solving skills, with the ability to operate effectively in ambiguous and rapidly changing environments.
• Exceptional written and verbal communication skills, including the ability to communicate effectively with technical and non-technical stakeholders.
• Demonstrated curiosity, learning agility, willingness to explore, and enthusiasm for emerging technologies.

Preferred

• Experience with Claude Code or other agentic AI development environments.
• Experience with GitHub Copilot, Microsoft Foundry, Azure AI, Azure OpenAI, Microsoft Copilot Studio, Power Platform, Power Automate, Power Apps, Logic Apps, or comparable technologies.
• Experience designing and deploying AI agents, multi-step agentic workflows, AI tools, and human-in-the-loop processes.
• Experience with RAG, vector databases, semantic search, enterprise knowledge management, document intelligence, model evaluation, AI observability, and prompt or context engineering.
• Experience evaluating multiple AI models or platforms according to solution quality, security, cost, latency, integration requirements, and enterprise suitability.
• Experience integrating enterprise platforms such as ERP, CRM, HRIS, procurement, warehouse management, eCommerce, or supply chain systems.
• Familiarity with Agile, DevOps, SDLC, enterprise architecture, cybersecurity, data governance, and Responsible AI practices.
• Experience supporting global business operations or working in electronics, technology, distribution, manufacturing, logistics, or supply chain environments.

Professional Competencies

AI-Native Engineering: AI is a natural and significant part of how you work. You use tools such as Claude throughout the engineering lifecycle to research, explore, design, develop, test, debug, document, and improve solutions. You understand how to provide AI with appropriate context, assess the quality of its output, recognize its limitations, and apply human engineering judgment when necessary.

Curiosity & Exploration: You are motivated by unfamiliar problems, ask thoughtful questions, challenge assumptions, and regularly experiment with emerging technologies. You explore multiple approaches before settling on a solution and continuously expand your technical and business knowledge.

Rapid Experimentation: You favor working prototypes and evidence over lengthy theoretical analysis. You use AI to explore alternatives quickly, validate assumptions, learn from unsuccessful approaches, and progressively evolve promising concepts into production-quality solutions.

Communication: You can explain complex ideas simply, facilitate productive business discussions, collaborate across technical disciplines, and clearly communicate technology choices, trade-offs, limitations, risks, and expected outcomes.

Engineering Judgment: You understand that AI-generated output must be reviewed and validated. You can distinguish between a convincing demonstration and a secure, maintainable, production-ready enterprise solution.

Bias for Action: You are comfortable moving quickly from problem discovery to experimentation and from successful experiments to production. You learn through building, testing, measuring, and iterating.

Business Impact: You measure success through business outcomes and demonstrable delivery improvements, including faster prototyping, reduced engineering effort, shorter iteration cycles, better decisions, improved productivity, reduced friction, and transformed ways of working.

Collaboration: You work effectively across business and technology teams, openly share knowledge, contribute reusable capabilities, and help other employees and technology teams adopt effective AI-assisted working practices.

Why join us

Our approach to employee wellness is holistic, which is why alongside competitive salaries, and excellent health and dental benefits, corporate employees have access to the following:

• Wellness amenities such as an onsite gym, fitness studio
• Group RRSP plan to take care of the future you
• Onsite medical clinic and telemedicine
• Daily shuttle bus from two downtown locations
• Subsidized employee cafe with delicious meals and snacks

Future Electronics is taking a hybrid approach to work. We value collaboration and working together, which our offices facilitate, combined with working remotely.

Future Electronics is an equal opportunity employer.