You will own the end-to-end delivery of Squid AI’s platform and AI agents into enterprise customer environments. This means you own the program: deployment milestones, solution quality, customer coordination, and post-launch success. You are the bridge between what our engineering team builds and the value our customers experience in production.
This is not a support role. You are the delivery leader, accountable for making sure every deployment works, every customer is confident, and every engagement creates a reference. You’ll need deep fluency in AI systems, sharp communication skills, and the creativity to solve problems that don’t have playbooks yet.
WHAT YOU'LL OWN
Delivery Program
- Own the deployment lifecycle from kickoff to production go-live. You set the milestones, track progress, and hold internal and external teams accountable to timelines.
- Design and execute UAT aligned to real customer workflows. Identify edge cases, hallucinations, and integration gaps before the customer ever sees them.
- Ensure solutions are robust under real-world conditions such as handling incomplete data, system variability, and evolving requirements.
Customer Relationship & Success
- Be the primary point of contact for enterprise customers during deployment and early production. Own the relationship, not just the tasks.
- Lead onboarding: build documentation, training, and enablement that gets customers self-sufficient fast.
- Proactively monitor adoption and performance post-launch. Build value dashboards that make ROI visible to the customer.
- Ensure customers hit initial value milestones within the first deployment phase. If they’re not, you’re the one who figures out why and fixes it.
Cross-Functional Coordination
- Coordinate across engineering, product, and sales to deliver on customer commitments. You are the voice of the customer internally.
- Translate customer feedback into actionable product insights. Surface deployment patterns that improve scalability.
- Transition stabilized accounts to Customer Success with full context so nothing drops.
- Lead and prioritize a team of Forward Deployed Engineers
- Present, communicate with C-Level executives
This is a role for someone who wants to be at the center of making AI systems actually work in the real world. Not in demos or in theory, but in production and at enterprise scale.
- (See above)
- AI fluency. Strong working knowledge of LLMs, agentic AI, RAG patterns, and enterprise integration architectures. You don’t need to write the models, but you need to know how they behave, fail, and get tuned.
- 3+ years in software implementation, solutions engineering, technical program management, or a comparable customer-facing technical role involving complex multi-system deployments.
- Exceptional communication. You translate complex technical concepts into clear, actionable language for executives and end-users alike.
- Creative problem-solver. Enterprise deployments rarely go exactly to plan. You find the path forward when data is messy, requirements shift, and systems don’t cooperate.
- Ownership mindset. You take responsibility for outcomes, not just activities. If the deployment isn’t working, it’s your problem until it is.
- Preferred: Experience deploying AI/ML or data-intensive SaaS products into enterprise environments.
- Preferred: Familiarity with CRM, ticketing, ERP, and document management systems as integration targets.
- Preferred: Comfortable operating in early-stage environments where process, tooling, and requirements are still being built.