AI Architect & Engineer

Position: Senior AI Architect & Engineer

Location: Remote

Duration: 6 month contract to hire

JOB DESCRIPTION:

Must have EXCELLENT comm skills

Target conversion:  $180,000 - $200,000

Interview: Virtual and perhaps onsite as final

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.