Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities

As an Associate AI Engineer, you will support the development, testing, deployment, and maintenance of artificial intelligence and machine learning solutions. Working alongside data scientists, engineers, and cross-functional teams, you will help prepare data, train and evaluate models, contribute to experimentation with new AI techniques, and support the deployment of AI solutions into production environments. This is an entry-level opportunity for recent graduates who bring strong technical fundamentals, curiosity, and a willingness to learn in a fast-paced, team-based environment.

Your Primary Responsibilities Will Include

Support AI and Machine Learning Development: Assist in building, testing, and improving AI and machine learning models using tools such as TensorFlow, PyTorch, and Scikit-learn. Collaborate on Model Improvement: Work with data scientists and engineers to optimize model performance, improve solution quality, and support AI engineering workflows. Assist with Data Preparation and Evaluation: Contribute to data preprocessing, feature engineering, model training, validation, and evaluation activities. Participate in AI Research and Experimentation: Support research, prototyping, and experimentation with emerging AI methods, models, and tools. Help Deploy and Maintain AI Solutions: Assist with deploying, monitoring, and maintaining AI models in development and production environments. Create Documentation and Share Knowledge: Develop technical documentation and contribute to team knowledge sharing related to models, experiments, and implementation practices. Apply Responsible AI Practices: Follow coding standards, data management practices, and responsible AI principles including privacy, fairness, and appropriate model governance. Work Across Teams: Collaborate with developers, data engineers, product managers, and other stakeholders to support end-to-end AI solution delivery. Stay Current with AI Trends: Continue learning about emerging AI technologies, tools, and industry trends to support innovation and growth.

Required Technical And Professional Expertise

Graduated within 24 months (MBA not accepted) Agentic and Generative AI: Experience using Generative AI and agentic AI tools to support software development, documentation, workflow automation, prompt design, experimentation, and knowledge retrieval. Ability to apply these tools responsibly with appropriate attention to accuracy, validation of outputs, data privacy, security, ethics, and human oversight. Programming Skills: Proficiency in at least one programming language, with Python preferred, or Java or C++. Machine Learning Foundations: Strong foundation in core machine learning concepts and supporting mathematics, including statistics, linear algebra, and basic calculus. Deep Learning and Neural Network Fundamentals: Understanding of deep learning basics and common neural network architectures. AI Domain Awareness: Familiarity with one or more AI domains such as natural language processing (NLP), computer vision, or conversational AI. Data Preparation and Model Evaluation: Knowledge of data preprocessing, feature engineering, model training, and model evaluation techniques. Large Language Models and Prompt Engineering: Experience with large language models (LLMs) and prompt engineering concepts and practices. RAG and Vector Database Exposure: Hands-on experience with vector databases and retrieval-augmented generation (RAG) approaches. AI/ML Frameworks: Hands-on exposure to machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. Development Tools and Practices: Experience with development tools and practices such as Git, Jupyter Notebook, and VS Code. Cloud and Deployment Awareness: Basic understanding of cloud and deployment practices including AWS, Azure, or GCP, along with awareness of CI/CD and MLOps fundamentals. Responsible AI Principles: Understanding of responsible AI principles, including ethics, privacy, fairness, and appropriate use of AI systems. Analytical and Collaborative Skills: Strong analytical and problem-solving skills, with the ability to communicate effectively and collaborate with both technical and non-technical stakeholders. Learning Agility: Ability to learn quickly, adapt in a fast-changing AI environment, and build technical and functional expertise in a consulting-oriented setting

Preferred Technical And Professional Experience

Exposure to model deployment, inference services, or production AI environments through coursework, internships, or projects. Familiarity with MLOps tools and workflows, including model versioning, experiment tracking, and monitoring. Experience with cloud AI services or managed machine learning platforms on AWS, Azure, or Google Cloud. Exposure to conversational AI, AI agents, or multi-step orchestration frameworks. Familiarity with data engineering concepts, APIs, or data pipelines used to support AI solutions. Experience contributing to technical research, proof-of-concept work, or applied AI experimentation.