AHEAD
Agentic Solutions Architect
$160K–$180K
United States · Remote · Full-time · Posted Oct 6, 2026
- Experience
- Significant experience is required; no specific number of years stated.
About the job
Architects and helps deliver production-grade agentic solutions across multiple squads, aligning business needs with platform standards, secure engineering, and measurable outcomes.
What you'll do
- Own architecture for a portfolio of agentic applications, including workflows, orchestration, integrations, security, and operational requirements.
- Translate business needs into technical options, implementation plans, architecture guidance, and cross-squad reusable patterns.
- Work hands-on with delivery teams, directly manage engineers, and coordinate decisions, dependencies, and risks across stakeholders.
What they ask for
- Experience with AWS and cloud-hosted foundation models or agent platforms, such as Amazon Bedrock or AgentCore, or equivalent technologies.
- Experience with Python, TypeScript/JavaScript, REST or GraphQL APIs, event-driven systems, serverless or containerized workloads, and infrastructure as code.
- Significant software or solutions architecture, technical leadership, and enterprise systems delivery experience; experience shipping production AI, generative AI, agentic, conversational, or intelligent-automation solutions.
- Strong understanding of agentic patterns, enterprise integrations, secure-by-default design, and the ability to influence multiple teams and communicate architecture clearly.
Nice to have
- Experience with enterprise SaaS or business systems such as Salesforce, ServiceNow, Microsoft 365, or SharePoint.
- Experience establishing reusable architecture standards or governance, leading architecture reviews, relevant certifications, or coordinating engineers against a timeline.
Pay
USD 160,000–180,000 per-year-salary (base salary)
Tools
AWS, Amazon Bedrock, AgentCore, Python, TypeScript/JavaScript, REST, GraphQL, MCP
AI focus
Designs and delivers production agentic and AI solutions, including assessing when AI is appropriate and defining orchestration, tool use, RAG, model selection, controls, human approvals, evaluation, and fallback behavior.
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