Enterprise AI/ML Solutions Architect

Accenture

City of Albany (NY)

On-site

USD 130,000 - 190,000

Full time

4 days ago
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Job summary

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

The role spans the full solution lifecycle—assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and LLMs), and designing scalable data and DevOps/MLOps pipelines for training and production, ensuring quality and

Qualifications

  • Bachelor's degree or equivalent (minimum 12 years) work experience.
  • Minimum of 3 years of experience deploying ML models in production at scale.
  • Minimum of 3 years of experience in computer science foundations and system design.
  • Minimum of 1 year in distributed computing systems and cloud deployment.

Responsibilities

  • Formulate real-world problems into scalable AI/ML solutions.
  • Develop ML models and scalable data pipelines with DevOps/MLOps.
  • Customize Deep Learning and Gen AI models for business needs.
  • Engage in R&D of new AI/HPC algorithms and simulations for clients.
  • Handle large datasets with data preprocessing for training/production.
  • Justify model approaches and collaborate with cross-functional teams.

Skills

ML engineering
Distributed computing
Python
Cloud deployment

Education

Bachelor's degree or equivalent (minimum 12 years) work experience

Tools

TensorFlow
PyTorch
LLM APIs

Job description

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

The role spans the full solution lifecycle—assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and LLMs), and designing scalable data and DevOps/MLOps pipelines for training and production, ensuring quality and

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