Devops Architect

Dear Candidate,

Greetings!

This email is regarding an exciting opportunity with our organization for the role of  AI Engineer based in Bangalore.

Please find the Job Description and Company Profile attached below for your reference.

Role Title:

About Team :SDE 3 - DevOps Engineer (Architecture)

The Generative AI team collaborates closely with clients to understand their unique needs and to customize AI solutions that enhance their operational efficiency and product offerings. Our team of solution architects and consultants is at the forefront of AI technology, advising clients on best practices and innovative approaches to integrate AI seamlessly into their workflows.

Website Https: //www.telusinternational.com

SDE 3 - DevOps Engineer
Bengaluru
5 Days Working - Hybrid
Telus Digital

Responsibilities:

• Own the reliability, scalability, and performance of production systems and services.
• Design and implement highly available, fault-tolerant, and distributed infrastructure.
• Define and drive observability strategy, including monitoring, logging, and alerting.
• Build and maintain scalable CI/CD pipelines to enable fast and reliable deployments.
• Automate infrastructure provisioning and operational workflows using IaC tools.
• Lead incident management and root cause analysis (RCA) and implement preventive measures.
• Define and track SLIs, SLOs, and SLAs aligned with business and product requirements.
• Collaborate closely with engineering teams to improve system design, deployment processes, and operational excellence.
• Optimize cloud infrastructure for cost, performance, and efficiency.
• Own and improve on-call processes; mentor engineers in handling production incidents.
• Drive best practices for security, networking, and infrastructure reliability.
• Participate in architecture and design reviews to ensure system resilience and scalability.
• Document system architecture, runbooks, and operational processes.

Requirements:

• 8+ years of experience in DevOps, SRE, or cloud engineering roles.
• Proven experience building software using Python, Go, Rust, or JavaScript, with strong scripting capabilities.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure.
• Expertise in Infrastructure as Code tools like Terraform, Ansible, or CloudFormation.
• Strong experience with containerization (Docker) and orchestration (Kubernetes).
• Solid understanding of Linux systems, networking concepts, and security best practices (IAM, VPNs, firewalls).
• Experience with monitoring and observability tools like Prometheus, Grafana, ELK Stack, New Relic, or Datadog.
• Proven experience in building and maintaining CI/CD pipelines (CircleCI, ArgoCD, Jenkins, GitHub Actions, GitLab CI, etc. ).
• Deep hands-on experience owning cloud security end-to-end.
• Strong debugging and troubleshooting skills for complex production systems.
• Comfortable operating in fast-moving engineering environments, balancing long-term infrastructure investment with immediate operational needs.
• Experience owning and continuously improving on-call processes - including rotation design, escalation policies, runbook culture, and post-incident review cadence.

Nice to Have:

• Experience with large-scale distributed systems and microservices architecture.
• Experience building and running operators on Kubernetes / knowledge of internals.
• Hands-on experience with cost optimization and capacity planning in cloud environments.
• Experience mentoring engineers and driving engineering best practices.
• Prior involvement in architectural reviews and cross-team technical decision-making.
• Strong understanding of compliance, security standards, and DevSecOps practices.
• Experience building internal developer platforms or self-service infrastructure tools.
• Development exposure: Experience contributing to backend services or application code (e. g., APIs, microservices) with strong software engineering fundamentals.
• MLOps experience: Familiarity with deploying, monitoring, and managing ML models in production, including tools like ML pipelines, model versioning, and data workflows.