About this role

IBM is hiring a mid-level AI Engineer in the machine learning function based in Multiple Cities. The posting calls out experience with TensorFlow, PyTorch, scikit-learn, LLMs.

Role: AI Engineer
Function: machine learning
Level: mid
Track: Individual contributor
Employment: Full-time
Location: Multiple Cities
Department: Data & Analytics

Job description

from IBM careers

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

Join us and be part of a growing international team delivering AI solutions for enterprise clients. You will work on real-world use cases involving Large Language Models (LLMs), modern machine learning frameworks, and scalable deployment architectures.

This role focuses on building and operationalizing AI solutions in production environments, combining strong engineering practices with applied AI expertise. You will contribute to end-to-end solution delivery, from design to deployment, in a collaborative and fast-evolving environment.

Your responsibilities will include:

• Designing and developing AI/ML solutions using modern frameworks (PyTorch, TensorFlow, etc.)

• Integrating Large Language Models (LLMs) into applications and workflows

• Deploying and operationalizing AI services in production environments

• Managing model lifecycle (training, deployment, monitoring)

• Optimizing and scaling AI models for performance and reliability

• Collaborating with data scientists, engineers, and stakeholders

• Contributing to solution design and technical discussions