AWS Lead Platform Engineer

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 17 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. 

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. 

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as ING, Millenium Bank, Play, Wavin, Arabian Drilling, FedEX, Leroy Merlin, Edenred, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing

You will be:

• leading the DevOps/Platform Engineering team, coordinating technical delivery, mentoring team members, and supporting their development,

• designing, building, and maintaining a shared AWS platform across multiple accounts, regions, teams, and environments,

• driving the development and continuous improvement of container orchestration and CI/CD platforms,

• establishing standardized infrastructure provisioning, deployment strategies, and reusable engineering solutions,

• overseeing cloud infrastructure architecture, networking, security, and cross-account connectivity,

• driving infrastructure modernization, security improvements, and compliance initiatives,

• collaborating with architects and engineering teams on technical decisions, PoCs, migrations, and platform evolution,

• promoting engineering standards, cost optimization, knowledge sharing, and best practices across teams.

Your profile:

• extensive hands-on experience with AWS cloud infrastructure and platform engineering,
• practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.
• strong experience designing and operating multi-account and multi-region AWS environments,
• deep practical knowledge of Amazon EKS, Kubernetes, Amazon ECS, Fargate, Lambda, EC2, ECR, VPC, ALB, CloudWatch, EventBridge, and DynamoDB,
• strong experience with Terraform, including developing reusable modules and standardized Infrastructure as Code solutions,
• proficiency in GitLab CI/CD, GitLab Runners, Kubernetes executors, and reusable pipeline libraries,
• experience designing and maintaining containerized CI/CD execution environments,
• understand advanced deployment strategies, including blue/green deployments, canary releases, and automated production rollouts,
• strong knowledge of AWS networking, VPC architecture, cross-account connectivity, service-to-service communication, TLS/mTLS, and proxy solutions,
• experience with Python, Bash, AWS CLI, and infrastructure automation,
• understand DevSecOps practices and have experience integrating security and quality controls into CI/CD pipelines,
• familiarity with infrastructure and container security tools such as tfsec, Trivy, Lacework, Orca Security, and OWASP ZAP,
• understand PCI compliance requirements, security-by-design principles, and infrastructure governance,
• experience with automated testing and code quality tools such as Pytest, Behave, Hadolint, and Pylint,
• understand cloud architecture, microservices, shared platform services, and infrastructure modernization,
• experience with architecture documentation, ADRs, PoCs, technical evaluations, and migration planning,
• understand FinOps principles and have experience with AWS cost analysis, resource optimization, and rightsizing,
• proven experience leading or mentoring technical teams, coordinating delivery, and supporting team development,
• communicate effectively with architects, product owners, delivery managers, and engineering teams,
• familiarity with Agile/Scrum methodologies and can promote consistent engineering practices across teams,
• work from the European Union region and a work permit are required.

Nice to have:

• experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,

• interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision