Sr AI Architect/Engineer
Builds and operates TNS's secure, production-grade enterprise AI platform on AWS Bedrock (Claude models) — model hosting, RAG/agent orchestration, data connectivity, security, monitoring, and cost management — while embedding with lines of business to prototype and ship AI use cases. Core stack: AWS Bedrock, Claude, Microsoft 365, Entra ID, SharePoint.
- Compensation
- $150K–$250K / yr
- Location
- Kansas, United States
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Architecture
- Posted
Skills
Must have
- Strong software/solutions engineering background with hands-on production AI/LLM apps (RAG, agents, tool use, orchestration, evaluation)
- Direct experience with a managed model platform in production; AWS Bedrock strongly preferred
- Experience with the surrounding AWS estate (IAM, VPC, KMS, CloudWatch, PrivateLink)
- Build and operate data connectivity to enterprise systems with permission-aware retrieval
- Working command of enterprise security and data governance: identity and access, data protection, encryption/key management, AI-specific threat models
- Production operations instincts — instrumenting what you build, owning monitoring and on-call of live systems
- Cost discipline — treating inference spend as an engineering constraint
- Genuine business fluency and communication skills
- Demonstrated bias toward shipping and adoption, not just building demos
Nice to have
- Depth in Microsoft/Azure ecosystem alongside AWS (Azure AI, Copilot Studio, Power Platform, Entra ID, SharePoint/Graph)
- Experience in a regulated or security-sensitive industry — payments, telecom, or financial data
- Self-hosted or fine-tuned model experience, and a clear view of when it beats a managed API
- Infrastructure-as-code and CI/CD for AI systems (Terraform, pipelines, automated evals in the release path)
- Change-management or internal-consulting experience
- Prior forward-deployed or customer-facing engineering experience
Full description
An extraordinarily talented group of individuals work together every day to drive TNS' success, from both professional and personal perspectives. Come join the excellence!
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Overview
Researches, designs, plans, develops and/or evaluates new, advanced network technologies by translating customer requirements or strategic business objectives into final product design. Work typically includes in-depth assessment of the customer’s networked application needs, providing network modeling and simulation testing, consulting to further clarify needs, and engineering high-speed technology network transports to deliver application functionality.
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Responsibilities
Why this role exists
TNS is moving from AI governance and foundations into execution. The platform building blocks are in place or underway — Microsoft 365, Copilot, Entra ID, SharePoint, and Anthropic's Claude models via AWS Bedrock. What we don't yet have is a dedicated person who can both build and operate the secure, production-grade AI platform that lets AI work safely against our data and embed with our lines of business to turn that capability into measurable value.
This role does both. You'll be the technical engine and the on-the-ground partner who makes AI a productive, trusted tool across the enterprise — and who multiplies that impact by enabling others to build, not just building yourself.
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What you'll do
Your time will span two connected halves.
Build and operate the AI platformModel platform and private hosting. Own our Claude deployment on AWS Bedrock end to end — model access and versioning, region and data-residency decisions, provisioned throughput vs. on-demand, and the private, VPC-isolated hosting posture that keeps our data inside our boundary and out of third-party training. Evaluate and manage additional models where they fit, and maintain a routing layer so applications aren't hard-wired to a single model or provider.
Harness and orchestration management. Build and maintain the application harness that sits between our users and the models: prompt and context management, tool and function calling, agent orchestration, retrieval pipelines, guardrails and content filtering, caching, and fallback behavior. Own the evaluation harness too — regression suites, quality benchmarks, and the release process that lets us upgrade models without breaking what's in production.
Data connectivity. Design and build secure, governed connections between AI systems and TNS data — Microsoft 365 and Graph, SharePoint, databases, data warehouses, APIs, and line-of-business systems. Own the retrieval architecture, indexing and freshness strategy, and the permission-trimming that ensures every user sees only what they're entitled to see, with no leakage across boundaries.