Machine Learning Engineer

Job Responsibilities

The Machine Learning Engineer will design, build, deploy, and maintain production-grade machine learning and AI solutions that support business decision-making, operational efficiency, and digital transformation.
This role focuses on turning AI/ML prototypes into reliable, scalable, and secure production solutions. The engineer will work closely with data scientists, data engineers, application teams, business stakeholders, and governance teams to operationalize AI/ML use cases across commercial, operational, financial, and corporate domains.
The role is suitable for someone who is strong in engineering, practical problem-solving, and end-to-end delivery of AI/ML systems.

Key Responsibilities
•    Design, develop, test, and deploy machine learning models for business use cases 
•    Convert proof-of-concept models into stable production services
•    Support the full AI/ML lifecycle from experimentation to production
•    Monitor model performance, data drift, error patterns, and production reliability
•    Support feature engineering, feature reuse, and scalable data pipelines
•    Partner with application development teams to integrate ML outputs into existing systems, dashboards, workflows, and user-facing tools
•    Support generative AI and agentic AI use cases such as apply proper safeguards such as grounding, evaluation, human review, and content validation
•    Help assess AI risks, including data leakage, bias, hallucination, explainability, and operational misuse

Job Requirements

•    Tertiary education in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Information Systems, or a related field
•    At least 4 - 5 years of hands-on experience in machine learning engineering, data science engineering, software engineering, or related technical roles
•    Hands on experience with Databricks, Spark, MLflow, Python; proven ability to build production-grade ML APIs/services
•    Demonstrated success in deploying ML/AI use cases at scale
•    Experience working in cloud environment such as AWS, Azure, or GCP
•    Experience with MLOps practices, including model monitoring, drift detection, and automated retraining
•    Familiarity with data governance, responsible AI, cybersecurity, and privacy controls
•    Ability to explain technical concepts clearly to business users and senior stakeholders