About the Company
The Cloud & DevOps Lead is responsible for designing, building, and governing enterprise-grade cloud infrastructure and software delivery capabilities on AWS. This role leads the end-to-end delivery of cloud infrastructure automation, CI/CD pipeline engineering, and AI-assisted development practices that accelerate engineering velocity, improve deployment quality, and drive operational efficiency at scale.
About the Role
The Lead owns the technical strategy and hands-on execution across Terraform-based infrastructure as code, Harness-driven software delivery, and the adoption of AI engineering tools including OpenAI, GitHub Copilot, and AWS Bedrock. This role partners closely with product engineering, architecture, security, and business stakeholders to ensure cloud infrastructure and delivery pipelines are robust, secure, cost-efficient, and continuously improving.
Responsibilities
- Cloud Infrastructure Design & Ownership
- Lead the design, provisioning, and governance of AWS cloud infrastructure supporting enterprise workloads across development, staging, and production environments.
- Architect multi-account AWS environments using AWS Organizations, Service Control Policies (SCPs), and landing zone patterns for security, isolation, and governance.
- Design and own network architecture including VPC design, Transit Gateway, Direct Connect, ALB/NLB, Route 53, CloudFront, and PrivateLink.
- Govern compute and container infrastructure: EC2 Auto Scaling, ECS (Fargate/EC2), EKS, and Lambda — ensuring high availability, fault tolerance, and cost efficiency.
- Own cloud security posture: IAM least-privilege design, KMS key management, Secrets Manager, GuardDuty, Security Hub, and AWS Config rule enforcement.
- Lead cloud resilience design: multi-AZ and multi-region architectures, Route 53 failover routing, backup strategies, and DR runbook ownership.
Infrastructure as Code (Terraform)
- Own and enforce the Terraform IaaC strategy across all AWS environments, including:
- Reusable module library: design, versioning, documentation, and governance standards.
- Remote state management: S3 backend with DynamoDB locking, workspace strategy, and state isolation per environment.
- Policy as code: Sentinel, OPA, or Checkov guardrails for security compliance and cost controls.
- Terraform Cloud / HCP Terraform for team-based workflow governance, remote execution, and audit trails.
- Automated testing: Terratest or equivalent for infrastructure validation pre-deployment.
- Establish and enforce GitOps-based Terraform workflows: PR-driven plan/apply, branch protection, peer review gates, and drift detection pipelines.
- Drive the principle that all infrastructure changes are code-driven — eliminating manual AWS console modifications from governed environments.
- Mentor engineers on Terraform best practices, module composition, and IaaC code quality standards.
CI/CD Pipeline Engineering (Harness)
- Own the Harness platform adoption and pipeline engineering standards across delivery teams, including:
- Pipeline as Code: YAML-based pipeline definitions, reusable stage templates, and shared library governance.
- Deployment strategies on AWS: canary, blue/green, and rolling releases for ECS, EKS, Lambda, and EC2 targets.
- Harness Continuous Verification (CV): integration with Datadog, Splunk, and CloudWatch for automated deployment quality gates.
- Harness Feature Flags for controlled feature rollout, experimentation governance, and kill-switch management.
- Harness Chaos Engineering for pre-production resilience validation of AWS workloads.
- Instrument and report DORA metrics via Harness dashboards: deployment frequency, lead time for changes, MTTR, and change failure rate.
- Drive deployment pipeline maturity — shifting delivery ownership to engineering teams while maintaining governance standards.
AI-Assisted Engineering Adoption
- Lead the strategic rollout and governance of AI engineering tools across the team:
- GitHub Copilot: establish as the standard engineering assistant for Terraform authoring, Python/Shell scripting, Harness YAML, and code review.
- OpenAI API (GPT-4 / GPT-4o): integrate into engineering workflows for automated documentation, code generation, change impact analysis, and internal tooling.
- AWS Bedrock: prototype and deliver AI-powered internal tools using foundation models (Claude, Titan, Llama) with Bedrock Agents and Knowledge Bases.
- Define AI tool governance standards: prompt engineering guidelines, output review requirements, code acceptance criteria, and productivity measurement frameworks.
- Identify and prioritise engineering toil reduction opportunities best addressed through AI-assisted automation.
- Measure and report AI tooling impact on engineering velocity, code quality, and delivery throughput.
FinOps & Cost Engineering
- Embed cost engineering practices into infrastructure design and delivery workflows, including:
- AWS resource tagging standards enforced via Terraform modules and Harness pipeline gates.
- Cost anomaly detection and alerting using AWS Cost Explorer, Budgets, and Compute Optimizer.
- Rightsizing analysis for EC2, RDS, ECS, and managed services with actionable remediation.
- Reserved Instance and Savings Plans strategy aligned to workload forecasts.
- Establish FinOps-as-code principles: cost constraints and budget guardrails embedded in Terraform and pipeline approval gates.
- Produce regular cloud cost reporting and optimization roadmaps for technology and business leadership.
Team Leadership
- Lead and mentor Cloud Engineers, DevOps Engineers, and Automation Engineers across delivery squads.
- Collaborate with product engineering, data platform, security, and architecture teams to align cloud infrastructure with business and delivery objectives.
- Present cloud engineering health, delivery metrics, cost trends, and technology roadmap to senior leadership and clients.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical discipline.
