Get AI systems running inside client infrastructure — cloud, containers, CI/CD.
Navigate enterprise constraints: production readiness reviews, governance gates.
Debug what breaks in production: networking, DNS, connection pooling.
Set up observability, cost controls, and deployment pipelines.
Own the technical relationship with the client's IT, Infrastructure, and DevOps counterparts.
Get things done inside organizations where you control nothing and depend on everyone.

Expert-level in at least one cloud platform — AWS, GCP, or Azure.
Strong hands-on experience with Kubernetes in production environments.
Productionized data science outputs, deployed ML models, or run AI applications at scale.
Deep production debugging experience: networking, DNS, latency, connection pooling.
Managed technical relationships directly with client IT or DevOps counterparts.
Background reads: production engineer, systems engineer, SRE, or platform engineer.

Impact: Ship AI systems that don’t just demo well but run at scale in Fortune 500 enterprises.
Growth: Stay hands-on with cutting-edge frameworks while developing field-tested instincts.
Variety: Solve problems across industries, from finance to healthcare to defense.
Culture: Work in a team that prizes resilience, creativity, and winning over process.
Trajectory: Build both your technical and consulting muscles in one of the most demanding roles in AI delivery.

Forward Deployed Infrastructure Engineer

Key skills