Enterprise AI/ML Scientist — Gen AI & MLOps Expert

Accenture; Des Moines (IA); On-site; USD 83,000 - 152,000; Full time

Benefits offered by this job

Market-competitive benefits

Job summary

Accenture is seeking an AI/ML Computational Scientist to design, build, and operationalize enterprise AI/ML solutions, combining custom models with cloud services for production-ready outcomes. You will manage the full lifecycle from data assessment to scalable model deployment, with edge devices and HPC considerations.

The role emphasizes DevOps/MLOps pipelines, large-scale data handling, and collaboration with cross-functional teams to deliver end-to-end projects for clients across industries.

Qualifications

• Bachelor's degree or equivalent work experience with a strong foundation in computer science.
• Minimum of 3 years of experience as a machine learning engineer or scientist deploying models at scale.
• Experience with distributed computing systems, big data, or cloud-based deployments.
• Experience building and deploying AI/ML software to cloud environments.

Responsibilities

• Formulate real-world problems into scalable AI/ML solutions.
• Develop and deploy ML algorithms, models, and pipelines for training and production with DevOps/MLOps.
• Customize Deep Learning/Gen AI models for business use cases across data and infrastructure.
• Engage in R&D of new AI algorithms and compute models for client applications.
• Work with large datasets and apply preprocessing to ensure high-quality input.
• Implement and maintain data storage/retrieval for models and knowledge assets.
• Justify model choices in business terms.
• Collaborate with business and technical teams to deliver end-to-end projects.

Skills

Python

C++

Java

R

SQL

LLM APIs

Education

Bachelor's degree or higher

Tools

TensorFlow

PyTorch

LLM APIs

Job description

Accenture is seeking an AI/ML Computational Scientist to design, build, and operationalize enterprise AI/ML solutions, combining custom models with cloud services for production-ready outcomes. You will manage the full lifecycle from data assessment to scalable model deployment, with edge devices and HPC considerations.

The role emphasizes DevOps/MLOps pipelines, large-scale data handling, and collaboration with cross-functional teams to deliver end-to-end projects for clients across industries.