Senior AI Architect & Engineer | Remote

• 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.

• 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., * 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.

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