Sr AI Architect/Engineer

Transaction Network Services (TNS); Brasil; On-site; BRL 260,000 - 480,000; Full time

Job summary

Transaction Network Services (TNS) seeks an accomplished engineer to build and operate a secure, production AI platform on AWS Bedrock. You will own model hosting, orchestration, data connectivity, and guardrails to keep AI within our boundary and compliant.

You will partner with business lines to turn AI capability into measurable value, implement cost management, and develop monitoring and observability that informs decisions and demonstrates impact.

Qualifications

• Strong software/solutions engineering background with hands‑on experience building and deploying production AI/LLM applications.
• Direct experience with a managed model platform in production; AWS Bedrock strongly preferred.
• Demonstrated ability to build and operate data connectivity to enterprise systems with permission‑aware retrieval.

Responsibilities

• Build and operate the AI platform: model hosting, orchestration, and governance.
• Design secure data connectivity between AI systems and enterprise data with proper permissions.
• Implement monitoring, observability, and cost management for production AI deployments.
• Collaborate with lines of business to identify value from AI and drive adoption, not just build it.
• Establish patterns and guardrails to accelerate new use cases safely.

Skills

Production AI/LLM apps

RAG and agents

Model hosting/orchestration

AWS Bedrock experience

Data connectivity and security

Tools

AWS Bedrock

IAM

VPC

KMS

CloudWatch

PrivateLink

Job description

An extraordinarily talented group of individuals work together every day to drive TNS' success, from both professional and personal perspectives.

Overview

Researches, designs, develops, and operates secure, production AI platform capabilities by translating business objectives and departmental requirements into deployed AI solutions. Work typically includes assessing lines of business’ automation needs, building the data connectivity, security, and cost‑management infrastructure that lets AI work safely against company data, and engineering the model hosting, orchestration, and monitoring systems that deliver measurable application value.

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.

What You'll Do

Your time will span two connected halves.

Build and operate the AI platform

Model 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.

Security and access control. 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. Partner with security and governance so every deployment is auditable and compliant by design, with a full audit trail of prompts, outputs, and data access.

Monitoring and observability. Instrument the platform for production operations — latency, error rates, throughput and quota headroom, output quality and drift, guardrail trips, and anomalous usage. Build the dashboards and alerting that let us find problems before users do, and the usage telemetry that tells us which solutions are actually delivering value.

Cost management. 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.

Reusable foundations. 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.

Ship value with the business (forward‑deployed engineering)

• 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.

What You'll Accomplish In Your First Year

• Stand up a secure, monitored, cost‑managed AI platform on AWS Bedrock with governed connectivity to TNS data.
• Ship a portfolio of production use cases with documented, measurable value — reduced cost to serve, shorter cycle times, and volume absorbed without added headcount.
• Establish a use‑case intake and prioritization process so demand is triaged by value rather than by who asks loudest.
• Deliver AI cost transparency and attribution by application and line of business.
• Enable at least one capable AI champion in each line of business.

What You Bring (required)

• 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