Enterprise AI/ML Product Lead — Data & MLOps

Accenture PLC; København; On-site; DKK 900,000 - 1,200,000; Full time

Job summary

Accenture PLC seeks a senior 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—from needs assessment and data exploration to model selection (Deep Learning, Gen AI, LLMs) and scalable MLOps pipelines for training and production, ensuring quality, value, and reliability of deployed systems.

Qualifications

• Significant experience as a machine learning engineer or scientist deploying models in production at scale, including monitoring and auditing.

Responsibilities

• Formulate real-world problems into scalable AI/ML solutions.
• Develop and implement machine learning algorithms and scalable data pipelines for training and production with DevOps & MLOps.
• Customize Deep Learning and Gen AI models for business use cases based on data availability and infrastructure.
• Engage in R&D of new AI and HPC algorithms and simulations for client applications.
• Work with large datasets and apply preprocessing to ensure high-quality training input.
• Implement and maintain data storage and retrieval for models and knowledge.

Skills

ML/AI expertise

Client-facing

Cross-functional collaboration

Problem formulation

Research & development

Education

Bachelor's Degree or equivalent

Tools

Python

C++

Java

R

SQL

TensorFlow

PyTorch

Job description

Accenture PLC seeks a senior 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—from needs assessment and data exploration to model selection (Deep Learning, Gen AI, LLMs) and scalable MLOps pipelines for training and production, ensuring quality, value, and reliability of deployed systems.