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