About the Company: Our client is a pioneering deep tech company transforming how enterprises leverage artificial intelligence to automate complex analytics workflows. Following a successful Series A funding round , the company is scaling rapidly to meet growing demand from some of the world’s largest organizations.
Their platform delivers AI-powered digital workers — autonomous systems that perform sophisticated analytics and data science tasks continuously and intelligently. By replacing fragmented tools, manual consulting, and costly licenses, these digital workers enable enterprises to achieve insights 10x faster and at a fraction of the cost , with guaranteed outcomes and zero operational overhead.
The Role: As an Infrastructure AI Engineer , you’ll design, deploy, and manage the infrastructure that powers some of the most advanced AI systems in production today. You’ll work at the intersection of cloud computing, AI operations, and DevOps , ensuring scalable, secure, and high-performing environments for enterprise AI deployments.
This is a hands-on, client-facing role where you’ll collaborate with technical and business teams to bring cutting-edge AI agents and workflows to life across multi-cloud and on-premise environments .
What You’ll Do
- Architect, deploy, and maintain production-grade AI solutions across multi-cloud and on-prem infrastructures.
- Design and manage Kubernetes-based environments to support high-performance AI workloads.
- Implement best practices for availability, observability, scalability, and security in AI infrastructure.
- Collaborate with enterprise clients to understand deployment constraints and design tailored solutions.
- Integrate and operationalize LLMs, RAGs, MCPs, and agentic AI workflows into robust environments.
- Build and optimize CI/CD pipelines and infrastructure-as-code (IaC) frameworks (e.g., Terraform, Helm).
- Partner closely with AI engineering teams to ensure smooth delivery from prototype to production.
What You’ll Bring
- Strong background in cloud infrastructure engineering — experience with at least one major provider ( AWS, Azure, or GCP ).
- Proven ability to deploy and manage systems across hybrid or multi-cloud and on-premise environments.
- Expertise with Kubernetes, Docker , and container orchestration at scale.
- Familiarity with DevOps and site reliability engineering (SRE) best practices.
- Experience with CI/CD automation and infrastructure-as-code tools.
- Understanding of AI/ML deployment patterns and how to support model-driven workloads in production.
- Strong client-facing communication and problem-solving skills, with a forward-deployed engineering mindset .
Why Join
- Join a fast-growing Series A company at the forefront of AI infrastructure innovation.
- Work on cutting-edge, real-world AI deployments for top global enterprises.
- Collaborate with world-class engineers and AI practitioners in a high-performance culture.
- Be part of a team that values ownership, excellence, and impact — where winning together is the goal.
