AI/ML Associate Manager

Accenture Middle East; Saudi Arabia; On-site; SAR 260,000 - 520,000; Full time

Job summary

Accenture Middle East seeks an AI ML Associate Manager to design, develop and deploy enterprise AI/ML solutions across cloud, edge, and HPC environments.

You will lead scalable data pipelines, implement MLOps for model lifecycle management, and work with Generative AI, LLMs, and deep learning models to deliver production-ready AI systems. Collaboration with cross-functional teams is essential for success.

Qualifications

• 6-9 years of experience in AI/ML, Data Science, or related fields.
• Hands-on experience designing and deploying enterprise AI/ML solutions.
• Proficiency in Python and modern AI frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud AI services (Azure, AWS, GCP) and MLOps practices.

Responsibilities

• Design and develop AI/ML solutions using cloud-native services and modern frameworks.
• Build and maintain scalable data pipelines for training, deployment, monitoring, and production operations.
• Implement DevOps/MLOps practices for model lifecycle management and delivery.
• Fine-tune and deploy Generative AI, LLMs, and deep learning models for business use cases.
• Collaborate with data engineers, architects and stakeholders to deliver production-ready AI solutions.

Skills

Python

TensorFlow

PyTorch

Scikit-learn

Generative AI

Large Language Models

MLOps

Data pipelines

Kubernetes

Docker

Edge AI

HPC

Tools

Docker

Kubernetes

Job description

As an AI ML Associate Manager you will design develop and deploy advanced Artificial Intelligence AI and Machine Learning ML solutions that address complex business challenges You will leverage cloud AI services Generative AI technologies and modern MLOps practices to build scalable production-ready AI systems This role requires expertise in model development data engineering and AI platform capabilities to support enterprise-wide AI adoption

Key Responsibilities

Design and develop AI and Machine Learning solutions using cloud-native AI services and modern AI frameworks Build and maintain scalable data pipelines to support model training deployment monitoring and production operations Implement DevOps and MLOps practices to enable efficient model lifecycle management and continuous delivery Customize fine-tune and deploy Generative AI Large Language Models LLMs and Deep Learning models based on business requirements Develop AI solutions that operate across cloud environments edge devices and high-performance computing HPC infrastructures Evaluate model performance and demonstrate the business value accuracy and effectiveness of AI solutions Conduct research and development activities to assess emerging AI technologies algorithms and modeling approaches Work with large-scale structured and unstructured datasets applying data preparation and feature engineering techniques to improve model outcomes Design and implement efficient data model and knowledge storage solutions to support AI applications and retrieval capabilities Collaborate with data engineers architects and business stakeholders to deliver scalable and production-ready AI solutions Ensure AI solutions follow security governance and Responsible AI standards throughout the development lifecycle

6-9 years of experience in Artificial Intelligence, Machine Learning, Data Science, Data Engineering, or related fields.Hands-on experience developing and deploying AI/ML solutions in enterprise environments.Strong experience with Python and popular AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar technologies.Experience working with Generative AI, Large Language Models (LLMs), Deep Learning, and related AI technologies.Knowledge of cloud-based AI services on Azure, AWS, or Google Cloud Platform.Experience building data pipelines and implementing MLOps practices for model deployment and monitoring.Strong understanding of data engineering, data preprocessing, and model optimization techniques.Experience fine-tuning and deploying Generative AI and foundation models for business use cases.Knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, and AI agent frameworks.Experience with containerization technologies such as Docker and orchestration platforms such as Kubernetes.Familiarity with edge AI, distributed computing, and high-performance computing (HPC) environments.Experience with AI observability, model monitoring, and production support processes.Strong analytical, problem-solving, and stakeholder management skills.Relevant certifications in Artificial Intelligence, Machine Learning, Data Science, or Cloud Technologies are preferred.