Enterprise AI/ML Solutions Engineer

Accenture

Orlando (FL)

On-site

USD 83,000 - 165,000

Full time

3 days ago
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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 and third-party AI services to deliver production-ready outcomes.

Your role spans the full solution lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and

Qualifications

  • Bachelor's degree or equivalent (minimum 12 years) work experience or higher.
  • Minimum 3 years of experience as a machine learning engineer or scientist deployed in production.
  • Minimum 1 year of experience in distributed computing systems and cloud deployments.
  • Experience building and deploying AI/ML software to cloud environments.

Responsibilities

  • Formulate real-world problems into scalable AI/ML solutions.
  • Develop and implement ML models and scalable data pipelines with DevOps & MLOps.
  • Customize Deep Learning and Gen AI models for business use cases.
  • Research and develop high-performance AI algorithms and simulations.
  • Work with large datasets and ensure high-quality training data.
  • Implement efficient data storage and retrieval for models and knowledge.
  • Justify model approaches to business problems.
  • Collaborate with business and technical teams to deliver end-to-end projects.

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 and third-party AI services to deliver production-ready outcomes.

Your role spans the full solution lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and

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