Forward Deployed AI Engineer (Post-Sales)

Description:

We are looking for a highly technical, customer-obsessed Forward Deployed AI Engineer (Post Sales) to guide customers through the deployment, operation, and adoption of Client-AI's platform in complex on-prem or hybrid environments. You will become the trusted technical advisor for our most strategic customers, partnering closely with Sales, Research, and Engineering to drive successful deployments and long-term customer value. You'll bridge the gap between our core platform capabilities and the unique requirements of each customer's environment.

This role is ideal for someone who thrives in ambiguity, enjoys solving challenging distributed systems problems, and wants to build both deep relationships and scalable solutions within a fast-moving startup.

What You'll Work On

• Lead customers through onboarding, deployment, and production rollout of Client-AI's platform while serving as the technical owner for assigned accounts-driving architecture, execution, long-term adoption, and tailored technical success plans.
• Partner cross-functionally with Sales, Engineering, and Research to translate use-case requirements into actionable technical strategies, support early trials, relay customer feedback, and help shape roadmap priorities.
• Guide customers in designing scalable, secure workflows across compute, storage, networking, and distributed systems, providing ongoing reporting on deployment progress, workload health, usage metrics, and executive-level updates.
• Adapt and optimize Client-AI's platform across AWS, GCP, Azure, and on-prem Kubernetes environments, handling provider-specific APIs, storage systems, networking configurations, and compute orchestration-including tuning performance for network topology, storage tiering, and resource allocation in each environment.

About You

• 5+ years of experience in technical roles involving solution architecture, customer engineering, consulting, or technical program delivery.
• Strong background in distributed systems, data infrastructure, and/or on-prem or hybrid compute environments.
• Experience working with ML/AI workflows, designing or deploying systems involving Kubernetes, networking, data pipelines, or large-scale backend infrastructure.
• Proficiency in Python, SQL, or similar languages, with the ability to contribute to technical conversations and debug customer issues end-to-end.
• Experience leading complex technical projects with multiple stakeholders-translating business needs into clear architecture and execution plans.
• Deep hands-on experience with multiple cloud platforms (AWS, GCP, Azure) including their compute, storage, networking, and IAM services.
• Proven track record of adapting complex distributed systems to run across different infrastructure environments.
• Expertise in infrastructure-as-code and configuration management for multi-environment deployments.
• Required to travel to customer sites as needed to support critical deployments and customer engagements.