AI & Automation Delivery Engineer

AI & Automation Delivery Engineer – Wellabe

• Supports the AI & Automation Center of Excellence by building practical AI and automation solutions, prototypes, pilots, reusable components, and technical accelerators.
• Works with business, technology, architecture, data, security, governance, and process improvement partners to deliver solutions that are secure, supportable, measurable, and aligned with enterprise standards.
• Focuses on delivering immediate value while enabling distributed teams to reuse approved patterns and scale AI and automation responsibly.
• Builds AI and Automation Solutions, configures, tests, and refines AI and automation solutions using approved platforms, patterns, workflows, agents, prompts, connectors, integrations, and reusable components.
• Develops prototypes, pilots, proofs of value, and early production implementations with the AI & Automation Solution Architect, ensuring solutions are usable, secure, scalable, supportable, and aligned with approved designs.
• Builds lighthouse solutions and working examples that demonstrate practical AI and automation use cases and can be showcased, reused, replicated, or extended across the enterprise.
• Validates feasibility, user experience, integration needs, data dependencies, platform fit, risks, and production readiness through rapid experimentation, documentation, and lessons learned.
• Develops reusable automation patterns, workflow templates, prompt examples, connectors, scripts, configuration guides, sample solutions, and technical accelerators.
• Partners with business teams, process improvement teams, and product owners to define use case goals, workflow impacts, user needs, success criteria, requirements, technical gaps, and practical implementation plans.
• Supports implementation from intake through design, build, testing, deployment readiness, adoption, measurement, scale, and production handoff.
• Tracks progress, risks, dependencies, reusable assets, implemented automations, lessons learned, benefits realized, adoption, usage, efficiency gains, quality improvements, and other business outcomes.

• 3+ years of experience in application development, automation, workflow configuration, systems integration, low-code/no-code development, digital transformation, or related technology delivery required.
• Experience building or configuring business applications, workflow automations, integrations, scripts, dashboards, forms, approval processes, or productivity solutions required.
• Familiarity with AI, generative AI, automation, low-code/no-code tools, RPA, workflow platforms, APIs, data services, testing, documentation, deployment readiness, supportability, security‑by‑design, and operational handoff.
• Familiarity with responsible AI, prompt engineering, retrieval‑augmented generation, document intelligence, process mining, workflow orchestration, data privacy, security controls, auditability, monitoring, and production support concepts.
• Skill at hands on delivery and the ability to build practical AI and automation solutions that demonstrate measurable business value.
• Problem solving skills and the ability to troubleshoot implementation challenges and identify practical paths forward.
• Automation mindset and the ability to identify opportunities to reduce manual work, improve speed, reduce errors, and increase consistency through automation.
• Knowledge of reuse, scalability, supportability, and maintainability principles when building AI and automation solutions.
• Ability to convert business needs and solution designs into practical working capabilities.
• Knowledge of security, privacy, risk, governance, and responsible AI expectations during solution build and deployment.
• Ability to move work from idea to prototype, pilot, and production readiness with appropriate delivery discipline.
• Strong collaboration skills to effectively partner with business, technology, architecture, data, security, risk, compliance, and operations teams.
• Strong continuous improvement skills and ability to use feedback, lessons learned, and usage data to improve solutions, reusable assets, and delivery practices.