Senior AI Engineer

Introduction

IBM Software infuses core business operations with intelligence—from machine learning to generative AI—to help make organizations more responsive, productive, and resilient. IBM Software helps clients put AI into action now to create real value with trust, speed, and confidence across digital labor, IT automation, application modernization, security, and sustainability. Critical to this is the ability to make use of all data, because AI is only as good as the data that fuels it. In most organizations data is spread across multiple clouds, on premises, in private datacenters, and at the edge. IBM’s AI and data platform scales and accelerates the impact of AI with trusted data, and provides leading capabilities to train, tune and deploy AI across business. IBM’s hybrid cloud platform is one of the most comprehensive and consistent approach to development, security, and operations across hybrid environments—a flexible foundation for leveraging data, wherever it resides, to extend AI deep into a business.

Your Role And Responsibilities

Design, develop, and optimize applications using Python or Go with a focus on AI and ML-driven functionalities. Work with frameworks like Langchain, Langgraph, and Langsmith to build advanced language processing solutions. Implement and manage Kubernetes operators to support scalable, distributed systems. Develop and enhance Retrieval-Augmented Generation (RAG) workflows, including optimization for performance and accuracy. Research, design, and implement Machine Learning, Deep Learning, and Neural Network models to solve complex problems. Apply advanced techniques in Natural Language Processing (NLP) to enhance system capabilities. Collaborate with cross-functional teams to integrate AI/ML solutions into existing products and platforms. Stay updated on the latest trends in AI/ML and cloud-native technologies to ensure the use of best practices.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

Hands-on experience with Langchain, Langgraph, or Langsmith frameworks.

Solid understanding of Kubernetes operators and their deployment.

Strong expertise in Machine Learning, Deep Learning, and Neural Networks.

Proficiency in Natural Language Processing (NLP) and related libraries.

Knowledge of Retrieval-Augmented Generation (RAG) workflows and optimization techniques.

Familiarity with cloud-native technologies and distributed systems.

Excellent problem-solving skills, a collaborative mindset, and a passion for innovation.

Preferred Technical And Professional Experience

Prior experience working in AI/ML model optimization or inference pipelines.

Exposure to containerization tools such as Docker and orchestration platforms like Kubernetes.

Familiarity with vector databases and hybrid search solutions.