Workflow Automation Specialist

AI Implementation Specialist

This role is open to candidates based in LATAM, Africa, and Eastern Europe. Please note that as this role supports U.S.-based clients, candidates must be available to work during U.S. business hours aligned with the client's time zone.

The AI Implementation Specialist will help automate manual data processes and accelerate reporting across the business.

This role will focus on connecting existing systems and building practical automation workflows rather than building new AI models from scratch. The AI Implementation Specialist will connect the company's ERP, database, and reporting tools with modern AI capabilities to reduce manual work and speed up access to data across departments.

The immediate priority is improving reporting efficiency and integrating core systems, with more advanced agentic AI workflows as a secondary, longer-term goal.

Key Responsibilities

Systems Integration & Automation

• Connect AI tools and platforms to existing business systems, including the ERP, database, and reporting tools.
• Build automated workflows that reduce manual data handling and reporting work.
• Identify manual and repetitive data processes and design practical automation solutions.
• Build integrations and automations that connect multiple systems.

Reporting Efficiency

• Improve the speed and reliability of internal reporting to better serve departments across the business.
• Build workflows that reduce the volume of reporting requests handled manually by IT.
• Improve access to data for departments across the business.

AI Tools & Implementation

• Evaluate and recommend AI tools and platforms suited to the business's technology stack and goals.
• Apply AI tools and platforms to practical system integration and automation needs.
• Lay the groundwork for more advanced agentic AI workflows over time.

Collaboration

• Work closely with IT and business process stakeholders to prioritize and implement integrations.

Qualifications

Experience

• Mid-level experience with a strong underlying technical/software engineering background.
• Proven experience integrating systems such as ERP platforms, databases, and reporting tools.
• Practical, hands-on experience with AI tools and platforms.
• Experience building automation workflows that connect multiple systems.
• Experience with ERP platforms and SQL-based databases is a plus.
• Experience with business intelligence and reporting tools is a plus.
• Experience supporting IT and business process teams in a collaborative capacity is a plus.
• Applied experience connecting systems and building automation rather than purely theoretical AI knowledge.

Skills

• Ability to connect AI tools and platforms with ERP, database, and reporting systems.
• Ability to identify manual and repetitive data processes and develop automation solutions.
• Ability to evaluate and recommend AI tools and platforms based on the existing technology stack and business goals.
• Familiarity with SQL as a database technology.
• Familiarity with business intelligence and reporting tools.
• Ability to work with IT and business process stakeholders.

Technical Environment

• ERP system used as the core business system for operations data.
• SQL as the primary database technology.
• Business intelligence and reporting tools used for internal reporting and dashboards.
• AI platforms are tool-agnostic, with the priority placed on selecting appropriate AI solutions for integration and automation across the existing stack.

What Success Looks Like

Within the first 30 and 90 days, the goal is to reduce manual reporting and data work by connecting AI to existing systems and automating key workflows.

The intended workflow is:

Manual Reporting Request → System Integration → Automated Workflow → Faster Data Access → Reduced IT Bottleneck

Success means:

• Measurably improving reporting speed.
• Reducing manual workload.
• Reducing the burden created by high volumes of manual reporting requests handled by IT.
• Speeding up data access for departments.
• Establishing integrations and automations across existing systems.
• Laying a strong foundation for more advanced AI automation.