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Introduction
At IBM Finance & Operations, we are the backbone of IBM’s transformation driving efficiency, transparency, and smart decision-making across the business. Our teams provide the insight and discipline that guide strategy, ensure financial strength, and enable IBM to invest in innovation and growth. Working in Finance & Operations means combining analytical skills with collaboration and curiosity. You’ll partner with colleagues across functions and geographies, using data, technology, and process excellence to create solutions that improve performance and deliver measurable impact. IBM offers continuous learning, career development, and a culture that values diverse perspectives. Join us and be part of a global team that keeps IBM moving forward, while building your own future in a dynamic and evolving environment.
Your role and responsibilities
Deliver end-to-end data and AI-enabled analytics solutions by combining strong data engineering foundations with applied AI and agentic capabilities. The AI Solution Engineer builds, maintains, and optimises data pipelines and ETL workflows, extends them with AI components and orchestration tools, and contributes to the development and operation of AI agents that support business processes. The role also supports business intelligence solutions and the enhancement of machine-learning-based data enrichment and classification use cases.
Required education
Associate's Degree/College Diploma
Preferred education
Bachelor's Degree
Required technical and professional expertise
- Data Engineering & Pipeline Development
Strong hands-on experience in designing, building, and maintaining ETL processes and scalable data pipelines across relational and analytical data environments. Ability to model data structures, manage interfaces between source systems, and support data warehouses and reporting layers using Python and SQ, yaml, AI Coding tools, Agents - Applied AI & Agentic Capabilities
Practical experience working with AI-enabled and agent-based solutions, including building, testing, and supporting AI agents on enterprise AI platforms. Ability to extend existing data pipelines to enable AI-driven use cases such as automation, enrichment, and intelligent decision support. - Data Analysis, Text Mining & Enrichment
Proven ability to perform deep analysis of structured and unstructured data, including text mining and exploratory analytics. Experience applying machine‑learning‑based techniques for data enrichment and classification, with a strong understanding of how these outputs integrate into downstream business and reporting processes. - Analytics & Business Intelligence Enablement
Experience supporting analytics delivery through the design of reporting layers, dashboards, and ad‑hoc data products. Familiarity with business intelligence and data visualisation tools to ensure insights are accessible and usable by both technical and business stakeholders. - AI‑Enabled Development Practices
Ability to effectively utilise AI tools and assistants throughout the development lifecycle to improve productivity, solution quality, and performance, while maintaining strong engineering discipline and code quality standards. - Data Quality, Governance & Risk Awareness
Solid understanding of data integrity, validation, and quality controls within enterprise environments. Awareness of governance principles, audit readiness, and risk considerations when designing and operating data and AI solutions. - Technical Collaboration & Solution Delivery
Experience working in global, cross‑functional teams to translate business problems into robust data and AI solutions. Strong problem‑solving skills, attention to detail, and the ability to operate effectively across engineering, analytics, and business contexts
Preferred technical and professional experience
- End‑to‑End Analytics & AI Solution Delivery
Experience contributing to or coordinating analytics and AI‑enabled solution deliveries, including translating high‑level business needs into technical requirements, supporting deployment activities, and balancing multiple stakeholders and priorities in a global delivery environment. - Applied Statistical & Machine Learning Techniques
Practical exposure to statistical methods and machine‑learning techniques used in real‑world analytics scenarios, such as classification, enrichment, or pattern discovery. Ability to apply these techniques pragmatically in support of data pipelines, AI agents, or enrichment use cases rather than purely experimental data science work. - Data Visualisation & Insight Communication
Hands‑on experience with business intelligence and data visualisation tools to support dashboards, reporting layers, and analytical outputs. Ability to clearly communicate data‑driven insights and technical concepts to both technical teams and non‑technical stakeholders, ensuring adoption and business value - AI Platform & Orchestration Familiarity
Familiarity with enterprise AI platforms or orchestration/agent frameworks (e.g. agent‑based or workflow‑driven AI solutions) and an understanding of how these integrate with data engineering and analytics architectures. - Cloud & Modern Data Architecture Exposure
Experience or working knowledge of cloud‑based data platforms, distributed data processing, or modern analytics architectures, supporting scalable and resilient AI and analytics solutions.