AI/ML Computational Scientist: Production-Ready Solutions

Accenture; Oklahoma City (OK); On-site; USD 33,000 - 107,000; Full time

Benefits offered by this job

Medical, dental, vision

Life insurance

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

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

You will lead the full solution lifecycle—from needs assessment through deployment—develop scalable data pipelines with DevOps/MLOps, and collaborate with business and technical teams to ensure value and reliability of deployed systems.

Qualifications

• Bachelor's degree or equivalent with extensive work experience.
• Minimum 3 years of experience as a machine learning engineer or scientist deploying models at scale.
• Experience with distributed computing and cloud environments.
• Ability to design scalable data and ML pipelines with DevOps/MLOps.

Responsibilities

• Formulate real-world problems into scalable AI/ML solutions.
• Develop and implement ML models and scalable pipelines.
• Customize DL/Gen AI models based on business needs.
• Engage in R&D of new AI/compute algorithms and simulations.
• Work with large-scale datasets and preprocessing techniques.
• Maintain efficient data storage and retrieval for models.
• Justify model approaches in business terms.
• Collaborate with cross-functional teams to productionize solutions.

Skills

Machine learning

Deep learning

Gen AI / LLMs

DevOps / MLOps

Python

Cloud deployment

Big Data

Education

Bachelor's degree

Tools

Python

TensorFlow

PyTorch

C++

Java

SQL

Job description

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

You will lead the full solution lifecycle—from needs assessment through deployment—develop scalable data pipelines with DevOps/MLOps, and collaborate with business and technical teams to ensure value and reliability of deployed systems.