GenAI / Bedrock Architect

Numentica

GenAI / Bedrock Architect responsible for designing secure generative AI solutions on AWS. Leading architecture discussions and enabling customer teams in a remote setting.

Posted 7/7/2026contractRemote • California • 🇺🇸 United StatesSeniorLeadWebsite

Tech Stack

Tools & technologies

AWSServiceNow

About the role

Key responsibilities & impact

Define secure, repeatable AI reference architectures spanning Amazon Bedrock, Amazon Q Business, and Amazon QuickSight as appropriate to the selected use cases.
Design generative AI patterns, including foundation model selection criteria, prompt and grounding design, RAG pipelines, agentic workflows, and tool/function calling.
Establish the secure AI foundation: identity, role-based access, encryption, logging, data-boundary controls, and audit patterns, working with the Security Architect.
Define guardrails, content filtering, prompt-logging policy, human-review patterns, and responsible-AI controls, accounting for data classification and PHI/PII handling.
Set the model and retrieval evaluation strategy and quality metrics, including citation and grounding expectations, and oversee iterative tuning.
Produce production-readiness gap analysis and a hardening/productionization roadmap.
Lead architecture and security review workshops, demos, and enablement office hours with the customer and stakeholders, presenting trade-offs in clear, accessible terms.
Enable and upskill the customer's engineering teams through paired design work, including spec-driven development and MCP-based workflows.
Provide technical direction to AI Application Engineers and Data/Search Specialists and produce reusable architecture artifacts, reference patterns, and documentation.

Requirements

What you’ll need

Strong hands-on experience designing and delivering generative AI solutions, ideally on Amazon Bedrock or comparable foundation-model platforms.
Deep understanding of LLM application patterns: RAG, agents, prompt engineering, function/tool calling, grounding/citation, and model evaluation.
Solid grounding in the broader AWS stack, including IAM, networking, storage, encryption, logging, and security fundamentals, not just the AI services.
Experience defining guardrails, evaluation frameworks, and responsible-AI / governance controls, including data classification and PHI/PII handling.
Excellent communication and stakeholder-facing skills; able to lead architecture and security workshops and explain technical concepts to mixed audiences.
Proven ability to operate as a technical lead across a small delivery team.
Demonstrated experience enabling and upskilling customer engineering teams through paired delivery, not just shipping solutions.
Familiarity with spec-driven development workflows and MCP (Model Context Protocol) based tooling.
Please provide a list of current AWS certifications
Experience delivering AI solutions in regulated or public-sector / government environments.
Familiarity with Amazon Q Business and QuickSight, and with enterprise knowledge and BI patterns.
Experience integrating with the Microsoft 365 ecosystem (SharePoint, Exchange/Outlook, Teams, OneDrive) and tools such as ServiceNow, Jira, Dynatrace, or Wiz.
Background in MLOps, CI/CD, or infrastructure-as-code on AWS.

Benefits

Comp & perks

Health insurance
Flexible work hours
Paid time off

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

RAG PatternsPrompt EngineeringFunction/Tool CallingModel EvaluationData ClassificationPHI/PII HandlingSpec-Driven DevelopmentMCP-Based ToolingMLOpsCI/CD

Soft Skills

Excellent CommunicationStakeholder EngagementTechnical Leadership