Machine Learning Engineer

Gen

. Frame and contextualize machine learning problems .

Posted 11/4/2026full-timeKuala Lumpur • MalaysiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying machine learning models, with proficiency in Python and SQL, and a strong foundation in statistical analysis and predictive analytics. Capable of collaborating effectively with cross-functional teams to enhance machine learning systems and workflows.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingStatistical AnalysisMLOps Tools FamiliarityMachine Learning Libraries Proficiency

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningPredictive AnalyticsStatistical ModellingData AnalysisModel ExperimentationAlgorithm ImplementationTest-Driven DevelopmentSoftware Engineering

Soft Skills

Communication SkillsCollaboration Skills

Tools & Technologies

PythonSQLScikit-LearnXGBoostLightGBMAWSDockerKubernetesDVCMLFlow

Industry Keywords

Machine Learning SystemsModel GovernanceData PipelinesBusiness Impact Evaluation

Tech Stack

Tools & technologies

AWSDockerKubernetesPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Frame and contextualize machine learning problems
Research, build and design machine learning systems and models to solve business problems
Assess, understand and analyze data and select appropriate datasets for modeling and testing
Identify, prototype and implement machine learning algorithms and tools to improve products
Design, execute and monitor machine learning model experiments and metrics
Perform statistical analysis to evaluate and prove business impact of modeling improvements
Automate, architect and orchestrate machine learning processes and pipelines
Monitor, optimize and maintain machine learning solutions in production
Enrich machine learning libraries and model development frameworks
Collaborate with MLOps Engineers and Data Scientists on systems designs and architectures
Enforce test-driven development and engineering best practices
Develop tools and internal libraries for model governance across the machine learning development lifecycle

Requirements

What you’ll need

Strong mathematical, statistical or actuarial background
Good programming knowledge and software engineering skills
Hands-on experience in machine learning, predictive analytics and statistical modelling
Experience developing and deploying machine learning models in production
Proficient in Python and SQL
Proficient with machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM
Preferably experience building machine learning systems and workflows
Preferably familiar with MLOps tools such as DVC, MLFlow, Metaflow, Seldon, BentoML
Preferably familiar with AWS, Docker, Kubernetes
Solid communication and collaboration skills

Benefits

Comp & perks

Flexible working options
Time off
Competitive pay
Benefits
Well-being programs
Healthy debate, experimentation and continuous learning environment