AI Architect/Engineer

ECCO Select is a talent acquisition and consulting company specializing in people, process and technology solutions. We provide the talent behind the technology enabling our clients to achieve their goals. For more information about ECCO Select, visit us at www.eccoselect.com.

Position Title:     Senior AI Architect & Engineer

Location Information  

Remote, with potential for up to 2 days onsite

Position Responsibilities:

As a Senior AI Architect & Engineer, you will partner closely with the Enterprise Architecture & Engineering leadership to drive the design, implementation, and production scaling of advanced AI solutions. This is a highly technical, hands-on role focused on both architectural design and engineering execution across multiple concurrent, enterprise-grade AI programs.

Your responsibilities will include:

• AI Architecture & Pattern Implementation:
  • Implement and extend enterprise AI integration patterns, connecting AI platforms to a variety of business data sources (APIs, CRM, ERP, data warehouses, document repositories).
  • Contribute to the design of AI integration frameworks—governing large language models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, agent systems, and API layers.
  • Design and deploy AI agent patterns, covering multi-agent orchestration, tool-use workflows, memory architecture, and human-in-the-loop mechanisms.
  • Support AI-enabled Agile delivery frameworks, helping develop workflow designs and AI-enabled tooling to accelerate software development timelines.
  • Produce architecture decision records (ADRs), integration specifications, and documentation that adhere to enterprise governance standards.
  • Develop and enforce data governance patterns such as prompt versioning, evaluation frameworks, and readiness checklists for AI solutions.
• Prototype & Pilot Delivery:
  • Deliver AI prototypes that demonstrate integration efficacy and serve as blueprints for productization.
  • Lead pilot projects from architectural validation through operational handoff, ensuring non-functional requirement (NFR) compliance and production readiness.
  • Build and document RAG pipelines, including quality metric definition and optimization strategies.
  • Develop AI agent solutions with robust observability, governance oversight, and demonstrable business value.
  • Create prompt libraries, workflow SOPs, and project configurations for seamless adoption by engineering teams.
• Production Scale Support:
  • Embed with platform engineering teams to mentor, enforce architectural patterns, and champion operational best practices.
  • Develop repeatable deployment methodologies, operational runbooks, and extension guides that accelerate platform maturity.
  • Instrument AI workloads for cost, performance, and drift monitoring, in partnership with cloud governance teams.
  • Identify and mitigate risks associated with AI toolchain sprawl and technical debt.
• Governance & Standards:
  • Serve as a technical contributor to enterprise Architecture Review Board (ARB) processes for all major AI decisions and platform choices.
  • Help evolve data and AI working group standards by documenting integration and governance patterns.
  • Apply Zero Trust security principles across all AI integration and architecture artifacts.
  • Maintain all architectural documentation as living, auditable references.
• Use Case & Stakeholder Engagement:
  • Translate business requirements into actionable AI designs mapped to core enterprise value streams.
  • Build proof-of-concept demonstrations that validate technical differentiation to executive and business stakeholders.
  • Collaborate closely with innovation, product, and delivery teams to scope and prioritize AI initiatives.

Your engagement will shift between three postures as required:

• Architecture Contributor: Contribute designs, patterns, and documentation during early solution definition phases.
• Prototype Builder: Independently build and validate prototypes, instrument solutions, and prepare for handoff.
• Embedded Engineer: Partner with delivery teams to scale validated patterns into robust, production-grade AI capabilities.

Essential Skills, Experience

• AI & LLM Integrations: Deep experience with Model Context Protocol (MCP) design, REST APIs, event-driven integration, OAuth 2.0, and connecting AI models to enterprise data sources such as CRMs, ERPs, and document repositories.
• AI Agent Engineering: Proven expertise integrating multi-agent systems—task routing, orchestration, memory, human-in-the-loop patterns—and familiarity with frameworks such as LangGraph, AutoGen, CrewAI, or equivalents.
• AI-Enabled Workflows: Experience with role-based prompt libraries, LLM-assisted SDLC tooling, AI-augmented software delivery, and the ability to build repeatable, consumable delivery patterns.
• Enterprise RAG Pipeline Development: Hands-on experience with document ingestion, chunking, embedding models, retrieval optimization, hybrid search, and measuring and tuning retrieval quality.
• LLM Application Development: Production experience using LLM APIs for structured output, multi-turn conversations, system prompt design, and evaluation frameworks. Practical familiarity with providers like OpenAI, AWS Bedrock, Anthropic, and Azure OpenAI Service.
• AI Governance & Observability: Implementing AI model lifecycle practices, readiness and cost tracking, FinOps tagging, and explainability documentation for enterprise workloads.
• Cloud Architecture: Extensive AWS (Bedrock, Lambda, ECS, S3, RDS, API Gateway, IAM) and Azure (AI Foundry, Container Instances, Cosmos DB, OpenAI Service) hands-on experience.
• Software Engineering: Advanced proficiency in Python (required); Node.js/TypeScript strongly preferred. Working in AI application development, pipeline construction, and scripting.
• MLOps & Production Engineering: CI/CD for AI, Docker, prompt management, automated evaluation, and instrumentation for observability.
• Soft Skills:
  • Able to fluidly shift between architecture, prototyping, and embedded delivery engagement styles.
  • Clear communicator—able to explain technical tradeoffs to diverse technical and non-technical audiences.
  • Produces actionable architecture artifacts for engineers.
  • Practices intellectual honesty, clearly distinguishing proven capabilities from aspirational goals.
  • Highly self-directed, with strong stakeholder alignment and autonomy as needed.
  • Treats governance requirements as enablers for delivery, not overheads.

Qualifications:

• Bachelor’s degree in Computer Science, Software Engineering, or a related field; Master’s degree preferred.
• 6–8 years in software engineering, AI/ML engineering, or solution architecture, with at least 2–3 years directly on production large language model or generative AI systems.
• History of successfully deploying AI capabilities from prototype to governed, production-grade solutions.
• AWS Certified Solutions Architect, AWS Certified Machine Learning (or equivalent certifications) a plus.
• Experience with enterprise governance frameworks: Architecture Review Board, FinOps, or change management.
• Background in synthetic workforce or persistent agent platform development strongly preferred.
• Demonstrated contributions to AI-augmented software delivery and measurable delivery cycle compression.
• Prior experience in live events, trade shows, hospitality, or high-volume B2B enterprise platforms is a plus but not required.

ECCO Select is committed to hiring and retaining a diverse workforce. Our 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. Veterans of our United States Uniformed Services are specifically encouraged to apply for ECCO Select opportunities.

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