Genius Match

Our client is a fast-growing, fast-paced software development organization with a mission to build new ways of interacting with information. They leverage intellectual property and their cloud infrastructure expertise to address complex, real-time data and analytics needs in the enterprise. Their teams are comprised of successful people who enjoy solving problems, engaging in substantive technical discussions, and have a strong passion for their work. They have high expectations in terms of skill, motivation, self-organization, and productivity. They value people who excel at working in collaborative, virtual, and collocated groups, and who thrive in fast-paced Agile development environments.
 
About the Role:
We are seeking a Senior DevOps Engineer for our client. In this role, you will take ownership of building a new AWS tenant from scratch, including the full environment setup and ongoing management. You will play a key role in managing EKS clusters for Kubernetes workloads and implementing robust CI/CD pipelines using tools like GitHub Actions. This is a long-term engagement.
 
Responsibilities:
 

• Design, build, and maintain AWS infrastructure starting from the ground up.
• Manage EKS clusters and ensure high availability, scalability, and security of Kubernetes environments.
• Develop and maintain Infrastructure as Code (IaC) using Terraform/Terragrunt.
• Implement and manage CI/CD pipelines (e.g., GitHub Actions) to support seamless deployments.
• Monitor system performance, troubleshoot issues, and optimize cloud resources.
• Ensure best practices in cloud security, cost optimization, and reliability.
• Collaborate closely with development teams to align infrastructure with application needs.
• Provide technical leadership, guidance, and mentorship to junior team members when needed.

Requirements:
 

• 7+ years of professional experience in DevOps, Cloud Infrastructure, or related roles.
• Proven experience with AWS services and building secure, scalable cloud environments from scratch.
• Experience with setting up and administering ElasticSearch nodes on EC2 instances.
• Practical experience with Terraform (and ideally Terragrunt) for Infrastructure as Code (IaC).
• Hands-on experience building and maintaining Docker containers.
• Experience with AWS ECS and ECR for container orchestration and registry.
• Strong experience with Kubernetes and AWS EKS cluster management (desired especially if collaborating with internal platform teams).
• Familiarity with AWS core services such as ALB, networking, IAM, S3, EC2, Fargate, and other commonly used services.
• Solid knowledge of CI/CD pipelines and tools, preferably GitHub Actions.
• Good understanding of networking, cloud security, and monitoring best practices.
• Excellent problem-solving and troubleshooting skills.
• Comfortable working independently and proactively in a dynamic environment.
• Strong communication skills and ability to collaborate with distributed teams.
• Flexibility to work with US West Coast hours occasionally.
   

We Offer:
 

• Competitive market salary.
• Fully remote work.
• Convenient and somewhat flexible working hours.
• 28 days of paid time off per calendar year.
• The chance to work on meaningful, socially valuable products alongside a highly professional, US-based international team.
• Interesting technical challenges with opportunities to grow and learn.

Our client is the leader in the Early Childhood Education (ECE) sector, providing the most widely used developmental assessment and curriculum system in the US. 

They are early in shaping how AI changes the way they build and operate software. They have active initiatives in AI tooling cost-management and agentic development workflows, but their direction is still forming and will keep evolving as the field does. 

They are not looking for someone to execute a fixed plan, they are looking for an experienced practitioner who can validate the direction we are setting, bring expertise we do not have in-house, stay ahead of what is emerging, and help us adjust and grow as the landscape shifts. This person is hands-on enough to build and prove things directly, and senior enough that we trust their judgment on where to place bets.

What you will do

• Act as a hands-on technical partner across AI engineering initiatives, currently spanning tooling cost management, model routing and selection, and agentic development workflows.
• Pressure-test the approaches they are considering, tell them where they are wrong or where a better path exists, and back it with evidence rather than opinion.
• Bring outside expertise and patterns from how other organizations are solving these problems, so they are not learning everything the expensive way.
• Track what is emerging across models, tooling, frameworks, and techniques, and translate it into concrete recommendations on what they should adopt, defer, or ignore.
• Build and run proofs of concept to validate ideas quickly, then help them decide what to operationalize.
• Help mature their agentic development workflow so engineering teams can adopt it, including model routing and token efficiency.
• Partner with Infrastructure and Security so that what they build fits their architecture and meets their data privacy obligations.
• Help them build internal capability, so the expertise compounds with their team and does not leave when the engagement ends.

Required background

• 5+ years in software, platform, or ML engineering, with recent, direct experience building and operating LLM-backed systems in production.
• Demonstrated breadth across the current AI engineering stack: models and their tradeoffs, inference cost dynamics, gateways and routing, agentic workflows, and the AI coding tool landscape.
• A track record of forming a point of view on fast-moving technical decisions and being right often enough to be trusted with them.
• Strong hands-on engineering ability. This person builds, benchmarks, and ships, not just advises.
• Comfort operating with ambiguity and early-stage direction; able to bring structure rather than wait for it.

• Cloud and infrastructure fluency (AWS preferred, including Bedrock).

   

Strongly preferred

• Experience helping an organization adopt AI-first engineering practices, not just individual tool use.
• Hands-on model evaluation and routing design, and exposure to AI cost governance.
• Experience in regulated data environments. 
• A habit of staying current through real practice (building, writing, contributing) rather than headlines.

What this role is not

• Not a fixed-scope implementation seat. The work will change as their direction evolves, and they want someone who helps drive that change.
• Not a data science or model training role. The focus is applied AI engineering, integration, and enablement.
• Not a manager. Individual contributor, hands-on, with influence earned through expertise.