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