Forward Deployed AI Engineer

Reach Velocity - Emerging Technology ?? ??; California; On-site; USD 150,000 - 190,000; Full time

Job summary

Reach Velocity - Emerging Technology is seeking a Forward Deployed AI Engineer to lead customer deployments, production adoption, and technical integrations across cloud, on‑premises, and hybrid environments. You will serve as a trusted technical partner, translating customer requirements into scalable AI infrastructure solutions.

The role combines infrastructure engineering with customer-facing responsibilities, requiring strong collaboration with Sales, Research, and Engineering to drive

Qualifications

• 5+ years of experience across solution architecture, customer engineering, infrastructure engineering, technical consulting, or technical program delivery.
• Strong hands-on experience with distributed systems, data infrastructure, and cloud or hybrid compute environments.
• Experience deploying AI/ML workflows or complex back-end systems with Kubernetes, data pipelines, networking, and large-scale infrastructure.
• Strong proficiency in Python, SQL, or similar languages with end-to-end debugging abilities.

Responsibilities

• Lead customer onboarding, deployment, production rollout, and ongoing adoption of AI infrastructure platforms.
• Own technical engagements end to end, including solution architecture, deployment planning, and customer success.
• Design and optimize scalable, secure systems across compute, storage, networking, and distributed infrastructure.
• Deploy and adapt solutions across AWS, GCP, Azure, and on‑premises Kubernetes environments.
• Troubleshoot complex infrastructure issues involving networking, storage, compute orchestration, and performance optimization.
• Partner with Sales, Engineering, and Research teams to support technical evaluations, translate customer needs into solutions, and influence product roadmap priorities.
• Monitor deployment progress, workload health, resource utilization, and adoption metrics; communicate updates to technical and executive stakeholders.

Skills

Solution architecture

Distributed systems

Python

Kubernetes

Cloud platforms (AWS/GCP/Azure)

IaC

Stakeholder management

Travel willingness

Job description

Work Model: In-office, 4 days per week

Travel: Required for customer deployments as needed

We’re looking for a highly technical, customer-focused Forward Deployed AI Engineer to lead customer deployments, production adoption, and technical integrations across complex cloud, on-premises, and hybrid environments.

You’ll act as a trusted technical partner to strategic customers, working closely with Sales, Research, and Engineering to translate customer requirements into scalable solutions, troubleshoot complex infrastructure challenges, and drive long-term platform adoption.

This role is ideal for someone who combines strong infrastructure engineering skills with customer-facing experience and enjoys solving ambiguous, technically challenging problems.

Key Responsibilities

• Lead customer onboarding, deployment, production rollout, and ongoing adoption of AI infrastructure platforms.
• Own technical engagements end to end, including solution architecture, implementation, deployment planning, and customer success.
• Design and optimize scalable, secure systems across compute, storage, networking, and distributed infrastructure.
• Deploy and adapt solutions across AWS, GCP, Azure, and on-premises Kubernetes environments.
• Troubleshoot complex infrastructure issues involving networking, storage, compute orchestration, and performance optimization.
• Partner with Sales, Engineering, and Research teams to support technical evaluations, translate customer needs into solutions, and influence product roadmap priorities.
• Monitor deployment progress, workload health, resource utilization, and adoption metrics; communicate updates to technical and executive stakeholders.

Required Qualifications

• 5+ years of experience in solution architecture, customer engineering, infrastructure engineering, technical consulting, or technical program delivery.
• Strong hands‑on experience with distributed systems, data infrastructure, and cloud or hybrid compute environments.
• Experience deploying AI/ML workflows or complex backend systems involving Kubernetes, data pipelines, networking, and large‑scale infrastructure.
• Strong proficiency in Python, SQL, or similar programming languages, with the ability to debug technical issues end to end.
• Hands‑on experience across multiple cloud platforms, including AWS, GCP, and Azure, covering compute, storage, networking, and IAM.
• Proven ability to adapt and deploy distributed systems across different infrastructure environments.
• Experience with infrastructure as code (IaC), configuration management, and multi‑environment deployments.
• Strong communication and stakeholder management skills, with experience leading complex technical projects.
• Willingness to travel to customer sites for critical deployments and technical engagements.

Ideal Candidate Profile

A hands‑on engineer who can move between infrastructure architecture, debugging, customer conversations, and production deployment. You should be comfortable navigating ambiguity, solving distributed systems challenges, and turning complex customer requirements into reliable, scalable AI infrastructure solutions.