Senior DevOps Engineer responsible for designing, automating, and maintaining infrastructure, CI/CD pipelines, and cloud orchestration across AWS, Kubernetes, and Databricks environments.
Responsibilities
Design, automate, and maintain CI/CD pipelines using GitHub Actions, Jenkins and other tools
Build and manage cloud infrastructure with Terraform on AWS
Provision and manage Databricks workspaces on AWS
Configure Databricks governance and networking
Deploy and maintain containerized applications on Kubernetes and AWS EKS/ECS
Hands‑on engineer responsible for working directly with investment stakeholders to rapidly explore, prototype and deliver AI‑enabled portfolio management solutions.
Locations (2)
Dublin, Leinster, Ireland
London, England, United Kingdom
Responsibilities
Work directly with Portfolio Managers and other investment stakeholders to understand problems, explore opportunities and shape AI‑enabled solutions.
Independently take ideas from concept and rapid prototype through iterative user validation and towards production.
Use AI development tools extensively to design, generate, test, debug and refine software.
Rapidly experiment with different approaches, recognising when to iterate, pivot or stop.
Apply sound engineering judgement to review, validate and improve AI‑generated code and solutions.
Lead the design, deployment, and operation of cloud infrastructure and data platforms for AI applications, ensuring scalability, security, and cost efficiency.
Compensation
75 000€ à 85 000€ brut annuel (fixe) + jusqu’à 8 000€ de variable selon expérience et séniorité
Responsibilities
Deploy cloud infrastructure using IaC and lead cloud migrations
Build and operate data platforms including warehouses and lakehouses
Create CI/CD pipelines and MLOps/LLMOps tooling
Develop harnesses for agents: runtime, evaluation frameworks, observability
Lead the design and implementation of scalable, robust backend systems to support AI agents and observability tools, prototype and evaluate prompting strategies, collaborate with researchers and product engineers, provide white glove deployment support, build long‑term customer relationships, and champion Traversal’s mission in the field.
Compensation
We offer competitive compensation, startup equity, health insurance, and additional benefits. The U.S. base salary range for this full-time, in-person role in New York is $200,000–$275,000, plus equity and benefits.
Responsibilities
Lead the design and implementation of scalable, robust backend systems to support AI agents and observability tools.
Prototype and evaluate prompting strategies, reasoning workflows, and tool‑use policies for agents operating on large‑scale observability data and complex troubleshooting workflows.
Collaborate with AI researchers, product engineers, and infrastructure teams to drive cross‑cutting upgrades to our core platform and product offerings.
Provide white glove deployment support for the Traversal platform and products in enterprise environments.
Build long term relationships with customers and proactively identify new opportunities for AI deployment throughout the lifecycle of an engagement.
The starting salary range posted for this position is $149,100.00 to $218,100.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits.
Responsibilities
Partner with enterprise customers to understand technical requirements and business goals.
Design scalable AI solution architectures and guide customers through production deployments.
Build integrations, prototypes, and reusable tools that accelerate customer success.
Lead technical workshops, architecture discussions, and troubleshoot complex production issues.
Collaborate with Product and Engineering teams to share customer feedback and influence future product enhancements.
Requirements
Bachelor's in Computer Science
Nice to have: Deploying or supporting Generative AI, LLM, or Machine Learning applications in production
Nice to have: Experience with Kubernetes, cloud infrastructure, observability, or MLOps
Nice to have: Experience building customer-facing technical solutions or integrations
Nice to have: Experience creating reusable developer tools, frameworks, or open-source contributions
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