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Introduction
The Cognos Analytics team is looking for a Full-stack Software Developer (AI) to join us. We build an enterprise scale, industry leading BI solution that employs AI to allow users to better understand and make better decisions from their data.

We are on a journey to modernize our product suite, moving to a cloud-first offering while adopting a micro-services architecture. We’re transforming how we develop, test and deploy features through continuous integration and continuous delivery. The Cognos Analytics team is looking for you to help us on this journey.

Your Role and Responsibilities
As a Full-Stack developer, you will be part of a global team that builds and supports Cognos Analytics. From within the team, you will work using the Agile model, in a cooperative and innovative environment, interfacing with global teams. In this role:

• You’ll work in a dynamic, collaborative environment to understand requirements, design, code and test innovative applications, and support those applications for our highly valued customers.
• You’ll create products that provide a great user experience along with high performance, security, quality, and stability.
• Design and code services, applications and databases that are reusable, scalable and meet critical architecture goals.
• Create Application Programming Interfaces (APIs) that are clean, well-documented, and easy to use.
• Design, code and support technologies that injects AI features into watsonx BI Assistant offerings.
• Support and adapt code based on feedback from customers deploying watsonx BI Assistant in mission critical environments.

PLP&T_24

Required Technical and Professional Expertise

• Robust experience in designing and developing backend development with Python, Java and Javascript
• Have knowledge in AI/ML Fundamentals
• Experience with Kubernetes and Docker
• Experience with GitHub
• Good verbal and written English with strong collaboration, analytical and troubleshooting skills.

Preferred Technical and Professional Expertise

• Experience developing enterprise scale Business Intelligence tools
• Neural networks and transformers models in particular as applied to NLP
• Classical NLP
• Prompt Engineering
• Large Language Models, training validation, testing and deployment