Join a team deploying enterprise AI systems in complex customer environments. You will own implementations from technical discovery through production readiness, helping turn deployment challenges into reusable practices that support secure, reliable AI adoption. The opportunity suits either a hands-on individual contributor or an engineer ready to build and lead a team.
Responsibilities
Own customer deployments from technical discovery and scoping through production readiness and handoff.
Configure and troubleshoot Kubernetes, Helm, infrastructure, networking, identity, observability, and scaling in customer environments.
Deploy and configure LLMs and agentic workflows in production.
Turn recurring deployment issues into reusable tooling, patterns, and playbooks.
Work directly with engineering, security, compliance, and operations stakeholders to resolve technical blockers.
Partner with product and engineering teams to share deployment learnings and inform platform improvements.
For the team-building opportunity, help establish and lead the forward deployed engineering function.
Requirements
At least 2 years of hands-on, customer-facing engineering experience, including ownership of enterprise deployments from discovery through production readiness.
Production experience with Kubernetes, DevOps deployments, LLMs, and agentic workflows.
Practical experience configuring and troubleshooting Helm, infrastructure, networking, identity, and observability.
Ability to work directly with enterprise technical and operational stakeholders, including security and compliance teams.
Exceptional individual contributor experience or experience building a team as an early hire.
Experience with regulated enterprise or infrastructure environments, AI governance, enterprise SaaS implementation, or consulting is valuable.
Conditions
Base salary: $165,000 to $215,000 annually. Visa sponsorship is not available.
On-site in New York, New York. Candidates based in New York must hold U.S. citizenship.
