Enterprise AI/ML Scientist: Production-Ready Models & MLOps

Accenture; Miami (FL); On-site; USD 33,000 - 107,000; Full time

Benefits offered by this job

Medical, dental, vision

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

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

You will work with large datasets, develop scalable data pipelines for training and production, and implement DevOps/MLOps practices. Collaboration with business and technical teams is essential, with travel may be required based on client needs.

Qualifications

• Bachelor's degree or equivalent work experience (minimum 12 years).
• At least 3 years deploying ML models to production at scale.
• Experience with data engineering, system design and programming.

Responsibilities

• Formulate real-world problems into scalable AI and Machine Learning solutions
• Develop ML algorithms and scalable data pipelines for training and production with DevOps & MLOps
• Customize and apply Deep Learning and Gen AI models for various use cases based on business needs
• Engage in research and development of new AI algorithms and simulations at client sites
• Work with large-scale datasets and implement quality data preprocessing and storage

Skills

Machine learning

Deep learning

Python

Distributed computing

Cloud platforms

Education

Bachelor's degree or equivalent

MS or PhD in related field preferred

Tools

TensorFlow

PyTorch

Docker

Kubernetes

Spark

Job description

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

You will work with large datasets, develop scalable data pipelines for training and production, and implement DevOps/MLOps practices. Collaboration with business and technical teams is essential, with travel may be required based on client needs.