Senior Infrastructure Engineer

Accountabilities: Lead the design, implementation, and ongoing improvement of scalable, secure, and resilient cloud infrastructure across AWS and Azure, supporting AI and SaaS products. Define and drive the infrastructure architecture strategy, ensuring alignment with long-term business objectives and identifying technical risks before they affect operations. Establish and maintain best practices for infrastructure as code (IaC), CI/CD pipelines, and automated deployment processes. Identify bottlenecks in the software development lifecycle and develop automation solutions that improve engineering productivity and accelerate delivery, using AI-assisted tools or traditional approaches where appropriate. Act as a technical authority on cloud architecture, containerization, infrastructure automation, and observability. Lead incident response activities and post-incident reviews, focusing on identifying root causes and implementing lasting improvements. Proactively identify infrastructure weaknesses, reliability gaps, and opportunities to improve system performance, security, and scalability. Partner with engineering leadership and other teams to guide technical decisions, communicate trade-offs, and ensure infrastructure capabilities support evolving product needs. Promote strong engineering standards and operational practices while sharing knowledge and helping strengthen the wider engineering team. Requirements: 5–7+ years of hands-on experience in cloud infrastructure, DevOps, or Site Reliability Engineering (SRE). Expert-level AWS knowledge, including EC2, ELB, Auto Scaling Groups, RDS, S3, SQS, Lambda, IAM, VPC, CloudFormation, CDK, and Route 53. Working knowledge of Microsoft Azure, with the ability to operate across multi-cloud and multi-region environments. Strong Linux administration and networking knowledge, including VPNs, VPC peering, NAT, DNS, and firewalls. Extensive experience with infrastructure as code and container orchestration at scale, using technologies such as CloudFormation, AWS CDK, Docker, and Kubernetes. Proven expertise in CI/CD pipeline design and management, using AWS CodePipeline, GitLab CI, or comparable tools. Strong scripting and automation skills with Python, Bash, and Ansible. Deep understanding of monitoring, logging, and observability practices using tools such as AWS CloudWatch, Datadog, and Azure Monitor. Hands-on experience designing high-availability architectures and implementing auto-scaling strategies. Strong knowledge of AWS security practices, including IAM, encryption, Secrets Manager, and security auditing. Experience working with databases such as MySQL, PostgreSQL, and Redshift, as well as database replication strategies. Familiarity with serverless architectures and services such as AWS Lambda and Fargate. Excellent written and verbal English communication skills, with the ability to explain technical decisions and trade-offs to both engineering teams and business stakeholders. Ability to work independently in a remote environment, take ownership of complex problems, and deliver solutions with minimal supervision. Strong technical leadership, initiative, adaptability, and a collaborative approach to improving team performance. Preferred qualifications and experience: Background in fintech or another regulated, high-growth industry, particularly scaling infrastructure while maintaining reliability and compliance amid increasing user and transaction volumes. Demonstrated technical leadership and experience improving infrastructure processes and engineering standards. AWS Professional-level certifications, such as Solutions Architect Professional or DevOps Engineer Professional. Knowledge of event-driven architectures and services such as SNS, SQS, and EventBridge. Experience optimizing costs across AWS environments. Familiarity with compliance frameworks and regulations, including SOC, NIST, ISO, CCPA, and GDPR. Experience integrating AWS services with third-party observability and security platforms. Knowledge of single-tenant and multi-tenant architectures, as well as systems designed for client on-premises deployments. Familiarity with scalable and reproducible machine learning pipelines and tools such as Kubeflow, MLflow, and Amazon SageMaker. Experience in data engineering, large-scale data processing, and storage systems. Familiarity with Jira and Confluence, including effective ticket management and KPI tracking practices. Benefits: Fully remote working arrangement within the United States. Opportunity to join a high-growth, Series B organization and contribute to the evolution of its infrastructure and engineering practices. Competitive compensation, potentially including salary, future token rights, and/or equity, depending on individual preferences. Comprehensive benefits, including medical, dental, and vision coverage. Flexible paid vacation policy. Opportunity to collaborate with an international team of approximately 70 professionals and contribute to products serving a broad customer base.