Lead DevOps Engineer

Lead DevOps Engineer, Site Reliability

Who We Are

Welcome to TELUS Digital — where innovation drives impact at a global scale. As an award-winning digital product consultancy and the digital division of TELUS, one of Canada’s largest telecommunications providers, we design and deliver transformative customer experiences through cutting-edge technology, agile thinking, and a people-first culture.

With a global team across North America, South America, Central America, Europe, Africa, and APAC, we offer end-to-end expertise across eight core service areas: Digital Product Consulting, Digital Marketing Services, Data & AI, Strategy Consulting, Business Operations Modernization, Enterprise Applications, Cloud Engineering, and QA & Test Engineering.

From mobile apps and websites to voice UI, chatbots, AI, customer service, and in-store solutions, TELUS Digital enables seamless, trusted, and digitally powered experiences that meet customers wherever they are — all backed by the secure infrastructure and scale of our multi-billion-dollar parent company.

Location and Flexibility

This role can be fully remote for candidates based in Brazil, due to team distribution and occasional in-person opportunities. If you are based in São Paulo or Porto Alegre, you are welcome to work from one of our offices on a flexible schedule.

About the Role

Our CXAI Platform powers a portfolio of Generative AI products deployed into enterprise contact centers and BPO operations, environments where downtime, latency, or silent model degradation translate directly to commercial impact. As a Staff DevOps Engineer, Site Reliability, you'll lead the architecture and maintenance of the infrastructure and reliability practices that keep AI-powered systems performant, observable, and trustworthy under real production load, including redundancy, latency, and cost management.

This is a staff-level individual contributor role with broad mandate. You'll set technical standards across the platform, partner directly with product and engineering leadership, and have real ownership over how reliability shapes the roadmap.

What You'll Own

Platform reliability strategy: help define SLOs/SLIs for AI-powered services, including latency and quality SLOs for LLM inference paths, and build the error-budget discipline that lets product teams ship fast without breaking trust.

Cloud architecture on GCP: design scalable, secure infrastructure for distributed AI services, event-driven workloads, and multi-LLM-provider integrations

Observability for non-deterministic systems: build metrics, tracing, and alerting that surface not just "is it up" but "is it behaving correctly" for LLM-powered features (drift, regression, hallucination rates, tool-call failures)

Resilience engineering: circuit breakers, graceful degradation, multi-provider failover, and chaos/fault-injection practices for AI inference paths

Infrastructure-as-code and automation: Terraform-first, automated everything, no toil tolerated

Production readiness: define and enforce PRR-style standards across teams launching new AI products and features

Technical leadership: mentor engineers, drive architecture reviews, and shape the broader engineering culture around reliability

What You Bring

Significant infrastructure engineering experience combining DevOps and SRE disciplines at scale

Deep GCP expertise (AWS a strong plus); relevant cloud certifications welcome

Production experience with SRE fundamentals: SLO/SLI design, error budgets, toil reduction, blameless incident review

Strong background in distributed systems failure modes and resilience patterns

Expert-level infrastructure-as-code (Terraform), container orchestration (Kubernetes), and CI/CD

Hands-on with modern observability stacks (i.e., OpenTelemetry, Sentry) and AI-specific observability tooling (Arize, LangSmith, Braintrust, or similar)

Experience with API management platforms, particularly Apigee and Cloud Run

Comfort working across Python, Javascript, and Bash for infra tooling

Strong spoken and written communication in english with teams and stakeholders

Bonus Points

Presents production experience with LLM-provider integrations (OpenAI, Anthropic, Google, Azure OpenAI) and the reliability quirks of inference at scale, such as, rate limits, latency tails, provider failover, cost controls

Has experience with event-driven architecture experience (Pub/Sub, Kafka, EventBridge)

Shows understanding of chaos engineering practices (Litmus, Gremlin, or homegrown equivalents)

Holds one or more GCP certifications, such as Cloud Architect, Cloud DevOps Engineer, or equivalent.

Why This Role

You will have a clear technical mandate, direct partnership with product and engineering leadership, and real ownership over infrastructure that powers AI workloads in production. Reliability at this scale is not a support function, it is a first-class engineering discipline with direct commercial impact.

If you want to define how cloud infrastructure and site reliability engineering work together for a suite of AI-powered products at a critical growth stage, this is it.

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