Enterprise AI/ML Systems Engineer

Accenture; Walnut Creek (CA); On-site; USD 75,619,000 - 223,157,000; Full time

Benefits offered by this job

Health insurance

Job summary

Accenture is seeking an AI/ML Computational Scientist to design, build, and operationalize AI/ML solutions for enterprise clients, combining custom models with cloud services to deliver production-ready outcomes.

You will handle the full solution lifecycle—from data assessment to model customization (including Deep Learning, Gen AI, and LLMs)—and build scalable data and DevOps pipelines to ensure reliability and value of deployed systems.

Qualifications

• Bachelor’s degree or equivalent (minimum 12 years) work experience or 6 years with Associate’s Degree.
• Minimum of 3 years of experience deploying models in production at scale, with monitoring and auditing.
• Minimum of 3 years of experience in computer science foundations, system architecture, and programming.
• Minimum of 1 year of distributed computing experience (big data, HPC, cloud).
• Minimum of 1 year building and deploying AI/ML software to cloud environments.

Responsibilities

• Formulate real-world problems into scalable AI/ML solutions.
• Develop and deploy ML models and data pipelines for training and production with DevOps & MLOps.
• Customize DL and Gen AI models for business needs, including edge and HPC contexts.
• Engage in R&D of new AI algorithms and apply them at client sites.
• Work with large-scale datasets and preprocessing for high-quality inputs.
• Implement data storage and retrieval for models and knowledge.
• Justify model approaches in business terms.
• Collaborate with business and technical teams to achieve end-to-end goals.

Skills

Machine learning

Distributed computing

Data preprocessing

Education

Bachelor's degree or equivalent

Tools

Python

C++

Java

R

SQL

TensorFlow

PyTorch

Job description

Accenture is seeking an AI/ML Computational Scientist to design, build, and operationalize AI/ML solutions for enterprise clients, combining custom models with cloud services to deliver production-ready outcomes.

You will handle the full solution lifecycle—from data assessment to model customization (including Deep Learning, Gen AI, and LLMs)—and build scalable data and DevOps pipelines to ensure reliability and value of deployed systems.