Enterprise AI/ML Solutions Engineer

Accenture; Milano; In loco; EUR 80.000 - 110.000; Tempo pieno

Descrizione del lavoro

Accenture Milano seeks a hands-on ML Engineer to design, build, and operationalize AI/ML solutions for enterprise clients. You will lead end-to-end deployment with DevOps/MLOps, tailoring DL and Gen AI approaches to business problems while handling large datasets and scalable infrastructure.

The role emphasizes production-grade ML, real-time monitoring, and collaboration with cross-functional teams to deliver measurable business value in client environments.

Competenze

• Bachelor's degree or equivalent.
• Proven experience deploying ML models in production with monitoring and auditing.
• Experience with distributed computing, big data, HPC, and cloud-based AI/ML deployment.
• Hands-on experience building and deploying AI/ML software to a cloud environment.
• Strong written and verbal communication; client-facing in a consulting context.

Mansioni

• Formulate real-world problems into scalable AI/ML solutions.
• Develop and implement ML algorithms and data pipelines for training and production with DevOps/MLOps.
• Customize DL and Gen AI models for diverse use cases.
• Engage in R&D of new AI and HPC algorithms and simulations for client applications.
• Work with large-scale datasets and data preprocessing for high-quality training inputs.
• Implement and maintain efficient model storage and retrieval systems.
• Justify the value of model approaches in business problems.
• Collaborate with cross-functional teams to deliver end-to-end project goals and integration.

Conoscenze

Python

TensorFlow

PyTorch

Distributed computing

Big data

Cloud deployment

Communication

Client-facing

Cross-functional collaboration

Formazione

Bachelor's degree or equivalent

Descrizione del lavoro

Accenture Milano seeks a hands-on ML Engineer to design, build, and operationalize AI/ML solutions for enterprise clients. You will lead end-to-end deployment with DevOps/MLOps, tailoring DL and Gen AI approaches to business problems while handling large datasets and scalable infrastructure.

The role emphasizes production-grade ML, real-time monitoring, and collaboration with cross-functional teams to deliver measurable business value in client environments.