Senior AI Architect & Engineer

This is a contract to permanent role working a hybrid schedule.

Core requirements are hands-on LLM application development, MCP integration architecture, RAG pipeline implementation, AI agent design, and AWS cloud engineering. 6 8 years experience with 2 3 years in production generative AI.

Senior AI Architect & Engineer

Job Summary

We are seeking a Senior AI Architect & Engineer to serve as a technical partner to the VP of Enterprise Architecture & Engineering. This is a senior-level role for someone who operates effectively across architecture and engineering contributing to integration pattern design, building and delivering AI pilots, and working alongside platform engineering teams to scale validated capabilities into production.

This role supports five concurrent AI programs: an enterprise AI platform migration, an AI-powered attendee intelligence pilot, a synthetic workforce platform, an AI-enabled software delivery methodology, and a proposal automation capability. The posture shifts by project phase contributing to architecture design when defining integration patterns, building prototypes when proving those patterns against real systems, and working embedded with delivery teams when scaling to production.

The ideal candidate brings strong, current experience in large language model application development, AI agent implementation, enterprise RAG pipelines, and API-based integration patterns. They understand how to move AI out of proof-of-concept and into governed, observable enterprise production and have done it before. They work effectively within enterprise governance frameworks and produce architecture artifacts that engineering teams can build from.

Key Responsibilities

AI Architecture & Pattern Implementation

• Implement and extend MCP (Model Context Protocol) integration pattern connecting enterprise AI platforms to business data sources including event APIs, CRM, ERP, data warehouse, and document repositories.
• Contribute to AI integration framework design that governs how large language models, RAG pipelines, agent systems, and API layers connect to core platforms.
• Design and implement AI agent patterns including multi-agent orchestration, tool-use, memory architecture, and human-in-the-loop oversight for assigned programs.
• Support the AI-Enabled Agile Delivery framework contributing to workflow design, role-based AI SOP libraries, and tooling that compresses software delivery timelines.
• Produce architecture decision records (ADRs), integration specifications, and NFR documentation that meet ARB governance standards and serve as references for engineering teams.
• Contribute to data governance patterns including prompt versioning, evaluation frameworks, and production readiness checklists for AI workloads.

Prototype & Pilot Delivery

• Build working AI prototypes that prove integration patterns against real enterprise systems instrumented, documented, and built to be transferable to delivery teams.
• Own pilot delivery from architecture validation through production-ready handoff enforcing NFR compliance and ensuring operational readiness before scale.
• Implement MCP server integrations connecting enterprise AI platforms to business data sources using extensible patterns that delivery teams can build on.
• Engineer RAG pipelines with documented retrieval quality metrics, chunking strategies, embedding model selection rationale, and latency targets validated against real content.
• Build AI agent implementations with observable behavior, job-scoped tool access, and governance documentation sufficient for ARB review.
• Develop AI-Enabled Agile Delivery tooling prompt libraries, project configurations, and workflow SOPs that delivery teams can adopt independently.

Production Scale Support

• Work embedded alongside platform engineering teams to transition validated prototypes into production-grade capabilities providing technical guidance and pattern enforcement during scale-up.
• Build repeatable deployment patterns, operational runbooks, and extension guidelines that allow permanent engineering teams to maintain and extend AI capabilities.
• Instrument production AI workloads with cost tracking, performance monitoring, model drift detection, and explainability documentation.
• Partner with the Cloud Center of Excellence on FinOps tagging and cost attribution for AI workloads running on cloud platforms.
• Identify rationalization opportunities across the AI toolchain and flag platform sprawl before it creates technical debt.

Key Skills & Qualifications

Preferred Qualifications

• Education: Bachelor's degree in Computer Science, Software Engineering, or a related technical field. Master's degree preferred.
• Experience: 6 8 years in software engineering, AI/ML engineering, or solution architecture with a minimum of 2 3 years working directly on large language model or generative AI systems in production.
• Demonstrated history of delivering AI capabilities from proof-of-concept through production deployment with observable outcomes and governance documentation.
• Certifications: AWS Certified Solutions Architect, AWS Certified Machine Learning Specialty, or equivalent cloud and AI credentials.
• Enterprise governance: prior experience contributing to Architecture Review Board processes, FinOps accountability frameworks, or formal change management programs.
• Synthetic workforce or persistent agent platforms: exposure to autonomous agent systems beyond conversational AI job-scoped agents, tool-use governance, and observable execution is strongly preferred.
• AI-Enabled Delivery: experience contributing to AI-augmented SDLC workflows that produced measurable delivery compression.
• Industry context: background in live events, trade shows, hospitality, or high-volume B2B enterprise platforms is a differentiator, not a requirement.

We would love to have you join our team! ECCO Select is committed to hiring and retaining a diverse workforce. ECCO Select s policy is to provide equal opportunity to all people without regard to race, color, religion, national origin, ancestry, marital status, veteran status, age, disability, pregnancy, genetic information, citizenship status, sex, sexual orientation, gender identity or any other legally protected category.

Equal Employment Opportunity is The Law

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