AI/ML Engineer

Introduction
At IBM work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so let’s talk.

Your Role and Responsibilities
We are seeking an experienced AI Engineer to join the Asset Engineering team. In this role you will be responsible for building innovative AI-powered solutions that integrate with various systems and applications. You will also be working with the broader team to build analyse and improve the AI solutions. You will be working primarily in one of our key solutions called the IBM Inspection Suite. The set of products enables clients to use AI to inspect parts and equipment for defects or anomalies using models such as Yolo3 that are trained in formats compatible with mobile devices (iPhones Androids) such as CoreML and Tensorflow Lite. You will directly collaborate with the CTO of the asset and the technical team who architect develop enhance and govern the asset.

Required Technical and Professional Expertise

• Machine Learning and GenAI: Strong understanding of machine learning and GenAI concepts. Awareness of ethical implications in GenAI including bias fairness and privacy.
• Python and Data Science Libraries: Proficiency in Python and relevant data science libraries (e.g. NumPy Pandas scikit-learn). Practice on Graph database and Vector DB.
• Computer Vision: Yolo object detection classification models. Ideally segmentation and anomaly detection.
• Container Development: Solid hands-on experience in microservices development Relational DB development K8S and/or Redhat OpenShift especially with AWS.
• Large Language Models: Familiarity with LLMs including their architectures capabilities and limitations. Skill in crafting effective prompts to guide LLMs and obtain desired outputs. Ability to handle and prepare data for analysis including data cleaning normalization and feature engineering

Preferred Technical and Professional Expertise

• Experience in converting model formats to CoreML and Tensorflow Lite.
• Experience on PostgreSQL and Redis DB (or other service caching mechanism).
• AI agent/agentic workflow concepts.