Director OF Implementation

Responsibilities Own end‑to‑end implementation of The Machine across customer and agency environments, from technical scoping through deployment, integration, and adoption – including daily code writing. Stand up and lead a team of implementation engineers and solution architects, setting delivery standards and accountable for the quality and pace of every engagement. Translate customer requirements into deployment plans, working closely with the platform engineering team to surface gaps, prioritize integrations, and feed real‑world needs back into the roadmap. Design integrations against tools such as Figma, Slack, Teams, Adobe, and performance dashboards, ensuring The Machine works alongside existing stacks. Drive adoption past go‑live, defining success metrics for each engagement and steering teams toward it with the right enablement, instrumentation, and follow‑through. Partner across engineering, account, and customer stakeholders, coordinating reporting lines and expectations so delivery holds together as one effort. Qualifications Track record of leading technical implementations or professional‑services delivery for a software or platform product, ideally with demanding enterprise customers. Strong technical fluency across cloud, integrations, and APIs – able to architect deployments, debug integrations, and earn engineers’ trust. Excellent verbal communications and presentation skills, with experience owning customer relationships through delivery, including scope, timeline, and trade‑off conversations. People‑leadership experience, including in matrixed or cross‑functional structures where influence is as important as authority. Bias toward adoption and outcomes, with conviction about what separates a deployed tool from one a team actually uses. Comfort leveraging AI‑enabled development tools and workflows to accelerate engineering, automation, debugging, and operational tasks. Experience orchestrating multi‑step AI or agent‑driven workflows, selecting appropriate models, tools, and execution patterns for different use cases. Strong judgment reviewing and hardening AI‑assisted output for security, scalability, maintainability, and architectural fit. Experience building or maintaining prompts, evaluation frameworks, documentation, or operational context systems that improve engineering velocity and reliability. Familiarity with automated evaluation and feedback loops for AI‑enabled systems and workflows. Compensation The base compensation range for this role is $150,000 – $225,000 and spans multiple levels. Compensation is influenced by skill set, level of experience, budget, and location. #J-18808-Ljbffr