Introduction
As a Customer Success Engineer, you will design viable client solutions by leveraging current IBM Automation product capabilities and remove technical inhibitors to sales opportunities. You will work with clients to drive adoption and expansion of IBM products to accelerate client value.
Your Role And Responsibilities
Your primary responsibilities will include:
- Design Client Solutions: Design viable client solutions by leveraging current product capabilities, removing technical inhibitors to sales opportunities, and creating technical proof points through technical accelerators such as demonstrations, POTs, POCs, POVs, Workshops, Solution Design, and MVPs.
- Drive Customer Value: Drive customer value by activating entitlements, finding sponsors, conducting use-case workshops, and establishing measurable business outcomes with client sponsors and stakeholders.
- Develop Success Plans: Develop a success plan that describes deployment roadmap(s), milestones, and outcomes with client sponsors and stakeholders, and deeply understand clients main challenges to become a trusted guide for their modernization and adoption of IBM's technology portfolio.
- Deliver Technical Proof Points: Create and deliver technical proof points through technical accelerators to demonstrate the value of IBM products and solutions.
- Collaborate with Clients: Work closely with clients to understand their needs, provide technical expertise, and drive adoption and expansion of IBM Automation products.
Required Technical And Professional Expertise
- Technical Solution Design: Experience with designing viable client solutions by leveraging current product capabilities, including creating technical proof points through technical accelerators such as demonstrations, POTs, POCs, Workshops, Solution Design, and MVPs.
- Product Capability Expertise: Experience in leveraging current product capabilities to remove technical inhibitors to sales opportunities and drive adoption and expansion of products.
- Technical Accelerator Development: Experience with creating and delivering technical proof points through technical accelerators to demonstrate the value of products and solutions.
- Client Solution Deployment: Experience in developing deployment roadmaps, milestones, and outcomes with client sponsors and stakeholders to drive customer value.
- Technical Portfolio Understanding: Experience with understanding clients main challenges and becoming a trusted guide for their modernization and adoption of technology portfolios.
Preferred Technical And Professional Experience
- Serve as the technical authority for IBM's Automation Infrastructure Delivery (IDA) Automation portfolio — including Terraform knowledge to perform Infrastructure as a Code (IaC) along with Ansible that focus on configuration management and automation, Vault for secrets management (credentials, encryption keys, tokens etc) and good to have is Verify for Identity and Access Management — designing architectures that solve complex hybrid and multi‑cloud data challenges.
- Partner with sellers and client success teams to lead technical discovery, solution design, and value mapping, translating business outcomes into scalable automation and orchestrating architectures using IBM Automation Infrastructure Delivery (IDA) technologies.
- Act as the technical bridge from sale to success, ensuring solution designs transition cleanly into implementation, adoption, and long‑term value realization.
- Design and deliver technical enablement for clients through demonstrations, POCs, hands‑on workshops, and architecture reviews, showcasing real‑world use cases such as application cloud modernization, cloud migration, securing secrets management, and AI‑ready infrastructures and agentic applications.
- Apply deep experience with automation patterns—including Infrastructure as Code (IaC), Configuration Management Pattern, Policy-Driven Automation (Governance) – industrializing infrastructure delivery — making it: Faster, Consistent, Scalable and Low-risk.
- Influence product adoption and roadmap alignment by bringing field feedback to IBM product teams, contributing to best practices, reference architectures, and accelerators that improve client outcomes across the Data & AI portfolio.
