AI Engineer

Role Summary The AI Engineer designs, builds, deploys, and operates production-grade AI agents and AI-powered solutions for customers — and extends that engineering work into the enablement and training that drives real-world adoption. This is a senior, hands-on technical role spanning the full Application Lifecycle Management (ALM) lifecycle, with strong customer-facing capability across both delivery and enablement. Key Responsibilities Design, build, test, deploy, and operate AI agents for customers using a combination of low-code and pro-code approaches Develop customer-facing AI solutions across the full ALM lifecycle, including design, development, testing, deployment, and ongoing iteration Build and integrate multi-model AI agents, selecting and orchestrating models based on use case, performance, and cost considerations Design and implement Retrieval-Augmented Generation (RAG) solutions, including document ingestion, vector databases, indexing strategies, and retrieval logic Configure and integrate MCP servers and related AI infrastructure components required for secure, scalable agent execution Implement secure authentication and authorization patterns for AI agents, including identity, permissions, and service-to-service access Collaborate with customers to understand business requirements and translate them into scalable AI agent designs Apply sound engineering practices including version control, environment management, testing strategies, and deployment automation Troubleshoot and optimize AI agents for performance, reliability, and accuracy Partner closely with security, data, and adoption teams to ensure AI solutions are safe, compliant, and aligned with governance requirements Translate the engineering work into customer enablement — designing and delivering technical training, workshops, labs, and demonstrations that help business users adopt the AI solutions you build Deliver enablement sessions both virtually and on-site, adapting depth and language for executive, technical, and frontline audiences Document architectures, designs, and operational considerations as part of customer deliverables and enablement assets Required Experience & Qualifications 5+ years of experience in software engineering, application development, or AI/automation-focused engineering roles Hands-on experience building AI agents or AI-powered applications using low-code and pro-code frameworks Deep understanding of AI concepts and architectures, including model inference, orchestration, and agent design patterns Practical experience with MCP servers, agent runtimes, or equivalent AI execution frameworks Strong experience designing and implementing RAG architectures, including vector databases and retrieval pipelines Experience working with multi-model AI approaches, including selecting, integrating, and managing multiple models within a single solution Solid understanding of authentication, identity, and security controls in application and API design Experience applying ALM best practices including source control, CI/CD, environment promotion, and testing Ability to work directly with customers in solution design and delivery engagements Strong verbal communication and public speaking skills with the ability to confidently lead live workshops, demos, and training sessions Ability to translate complex or technical concepts into clear, practical learning experiences for non-technical audiences Comfort traveling for work and delivering on-site engagements as part of customer projects Strong problem-solving skills and comfort working in rapidly evolving technical domains Preferred Qualifications Experience building AI solutions in Microsoft-centric environments, including Copilot or Azure-based AI services Familiarity with AI governance, data security, and responsible AI principles Experience integrating AI agents with enterprise data sources and business applications Background in platform engineering, cloud infrastructure, or distributed systems Consulting or professional services experience delivering customer-specific solutions Experience collaborating with security, data, and compliance teams during solution design Experience designing role-based or persona-driven enablement programs for technical and business audiences Background in change management, user adoption, or workforce enablement initiatives Experience supporting executive or leadership-level briefings and demonstrations Interest in evolving toward AI architecture, solution engineering, or principal-level technical roles What Success Looks Like Production-grade AI agents and RAG solutions ship on time, perform reliably, and meet customer outcomes Customers see a single, credible technical lead from solution design through deployment and adoption Enablement materials and live sessions accelerate real-world usage of the AI solutions you deliver Engineering practices (ALM, security, governance) are applied consistently across engagements Customers and teams view you as a trusted technical authority on applied AI