Senior AI Platform Architect & Production Engineer

Transaction Network Services (TNS); North Carolina; Presencial; USD 150.000 - 250.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical and dental coverage

Life insurance

Paid holidays

401K with company match

Descripción de la vacante

Transaction Network Services (TNS) seeks a senior engineer to build and operate a secure, production-grade AI platform on AWS Bedrock. You will own model deployment, data connectivity, security, and cost governance across the enterprise.

You'll collaborate with lines of business to turn AI capabilities into measurable value, drive adoption, and mentor AI champions while maintaining strict governance and performance monitoring.

Formación

• Strong software / solutions engineering background with hands-on experience building and deploying production AI/LLM applications — RAG, agents, tool use, orchestration, and evaluation.
• Direct experience with a managed model platform in production; AWS Bedrock strongly preferred, along with the surrounding AWS estate (IAM, VPC, KMS, CloudWatch, PrivateLink).
• Demonstrated ability to 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 and key management, and AI‑specific threat models.
• Production operations instincts — you instrument what you build, and you've owned the monitoring and on‑call reality of a live system.
• Cost discipline: you treat inference spend as an engineering constraint and can show where the money goes.
• Genuine business fluency and communication skills — you're comfortable sitting with finance, operations, or sales, understanding their problems, and translating them into solutions.
• A demonstrated bias toward shipping and adoption, not just building demos.

Responsabilidades

• Own 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.
• 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.
• 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.
• Architect least-privilege access across identity, network, and data layers — IAM and Entra ID, private networking, encryption and key management, secrets handling, and tenant isolation. Defend against AI-specific threats: prompt injection, data exfiltration through model outputs, insecure tool use, and over-permissioned agents.
• Instrument the platform for production operations — latency, error rates, throughput and quota headroom, output quality and drift, guardrail trips, and anomalous usage. Build dashboards and alerting that let us find problems before users do, and the usage telemetry that tells us which solutions are actually delivering value.
• Own AI spend as a first-class engineering concern: token-level cost attribution by application and line of business, budgets and alerting, model right-sizing (using the cheapest model that meets the quality bar), prompt caching and context optimization, batch processing where latency allows, and provisioned-throughput decisions. Produce the chargeback and unit-economics reporting leadership needs to fund the portfolio with confidence.
• Turn all of the above into patterns, templates, and guardrails so each new use case is faster, cheaper, and safer to build than the last.
• Embed directly with teams across our lines of business (Communications, Waypoint, Payments, Corporate/Shared) to identify high-value AI and automation opportunities.
• Prototype quickly, validate with real users, and take the strongest ideas to production.
• Drive adoption — measure whether solutions are actually used and delivering value, and iterate until they are.
• Enable others: seed and support a network of "AI champions" in each line of business, equipping non-specialists to build their own automations on your patterns and under appropriate oversight.

Conocimientos

AI/LLM development

Production operations

Cost optimization

Business communication

Delivery focus

Security & governance

IAM & data governance

Herramientas

AWS Bedrock

Terraform

CI/CD

Descripción del empleo

Transaction Network Services (TNS) seeks a senior engineer to build and operate a secure, production-grade AI platform on AWS Bedrock. You will own model deployment, data connectivity, security, and cost governance across the enterprise.

You'll collaborate with lines of business to turn AI capabilities into measurable value, drive adoption, and mentor AI champions while maintaining strict governance and performance monitoring.