AI/ML Engineer: Build Scalable AI Solutions

Accenture Malaysia

Puchong

On-site

MYR 120,000 - 180,000

Full time

45 hours ago

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Job summary

Accenture Malaysia seeks an AI/ML Engineer to design, develop, and deploy machine learning solutions that drive business innovation and deliver measurable impact. You will work with cross‑functional teams to build scalable AI‑powered applications, optimize data pipelines, and implement ML models that support business objectives and cloud deployment.

The ideal candidate combines strong software engineering fundamentals with hands‑on experience in ML, data analytics, and cloud deployment, offering

Qualifications

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Mathematics, Statistics, or a related field.
Minimum 1 year of experience in Machine Learning, Artificial Intelligence, Data Engineering, or Software Engineering.
Proven experience developing and deploying machine learning models in production environments.
Strong knowledge of Big Data Analytics and data processing frameworks.
Proficiency in Python and common machine learning libraries such as TensorFlow, PyTorch, Scikit‑learn, or similar.
Understanding of software development lifecycle, version control, testing, and deployment methodologies.
Strong analytical, problem‑solving, and communication skills.
Experience building and maintaining ETL pipelines.
Hands‑on experience with Machine Learning Operations (MLOps) tools and practices.
Knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
Experience with Data Visualization tools such as Power BI, Tableau, or similar platforms.
Understanding of statistical analysis, predictive modeling, and experimentation techniques.
Familiarity with generative AI, large language models (LLMs), or AI solution deployment.

Responsibilities

Design, develop, test, and deploy machine learning models and AI‑driven applications for business use cases.
Build and maintain scalable data processing and ETL pipelines to support model training and inference.
Collaborate with data scientists, engineers, and business stakeholders to translate requirements into technical solutions.
Monitor, evaluate, and improve model performance, accuracy, scalability, and reliability
Implement MLOps best practices, including model versioning, automated deployment, monitoring, and governance.
Troubleshoot and resolve software, model, and data‑related issues to ensure smooth production operations.
Develop and maintain reusable code, technical documentation, and engineering standards.
Contribute to continuous improvement initiatives and support innovation through emerging AI/ML technologies and methodologies.