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
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