AI Deployment Engineer

• Customer-Facing
• Technical

The AI Deployment Engineer at CrewAI is a post-sales technical role responsible for turning signed deals into production success stories. You will own the end-to-end technical relationship with enterprise customers—from initial onboarding and integration through production deployment, optimization, and ongoing expansion.

This role is ideal for someone who finds deep satisfaction in solving hard infrastructure and integration problems, building lasting partnerships with customer engineering teams, and ensuring that multi-agent AI systems deliver measurable business value at scale.

Key Responsibilities

Technical Implementation & Integration

• Lead the technical integration of CrewAI's platform into customers' systems, including API integrations, data pipelines, authentication flows, and custom workflows
• Develop and maintain robust, scalable solutions tailored to each customer's infrastructure requirements, leveraging deep expertise in Python, Agentic AI Stack, and cloud platforms
• Troubleshoot complex technical issues during and after implementation—from container orchestration and networking problems to LLM configuration and tool integrations—providing timely resolutions and root cause analyses

• Develop and integrate custom agents, tools, and processes using Python and CrewAI's open-source and enterprise libraries
• Monitor deployed solutions for performance, reliability, and business value, rapidly iterating on agent roles and workflows to adapt to evolving customer needs

• Act as the primary technical point of contact for a portfolio of enterprise customers post-sale, building deep, trusted relationships with their engineering and leadership teams
• Conduct structured onboarding programs, technical workshops, and training sessions to drive product adoption and self-sufficiency
• Proactively identify expansion opportunities by understanding customers' evolving business objectives and mapping them to additional CrewAI capabilities
• Collaborate with Customer Success Managers and Support Engineers to ensure smooth operations and high retention

• Create and maintain deployment runbooks, best practices guides, architecture documentation, and customer-specific technical references
• Provide structured, actionable feedback to Product and Engineering based on real-world deployment patterns, pain points, and feature requests
• Contribute to internal tooling, automation, and processes that improve deployment efficiency and customer experience at scale

Qualifications & Desired Skills

• 3+ years in customer-facing technical role (Forward Deployed Engineer, Implementation Engineer, Technical Account Manager, or similar)
• Strong proficiency in Python and hands-on experience with containerized deployments (Docker, Kubernetes), and Agentic AI Stack (observability, RAG, etc)
• Familiarity with AI/ML concepts and technologies, including LLMs, AI agent frameworks, RAG patterns, and prompt engineering
• Experience troubleshooting distributed systems in production—networking, scheduling, resource management, and observability
• Exceptional communication skills, with the ability to translate complex technical issues into clear customer communications and executive briefings
• Knowledge of workflow orchestration, multi-agent systems, or distributed computing is a strong plus
• Bachelor's degree in Computer Science, Engineering, or a related technical field preferred
• Experience building GenAI solutions, working with various databases (SQL, NoSQL), or contributing to open-source AI agent projects is a significant bonus

You will work closely with Product, Engineering, Sales, and Customer Success to resolve technical issues, surface product improvements, support account expansion, and ensure long-term customer satisfaction. You'll also partner with the pre-sales team to ensure seamless handoffs from closed deals into successful implementations.

Performance Metrics

• Successful project implementations and time-to-production-value
• Customer satisfaction scores (CSAT/NPS) and account health metrics
• Timeliness and quality of technical support and issue resolution (SLA adherence)
• Net revenue retention (NRR) contribution through expansion and low churn
• Quality of solution designs, documentation, and actionable product feedback