AI/ML Engineer

GoTymeX

. Design, build, and optimize AI/ML pipelines for real-time and batch inference using modern MLOps practices .

Posted 11/5/2026full-timeHo Chi Minh City • VietnamMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing AI/ML pipelines, deploying models in production environments, and collaborating across teams to ensure model reliability and compliance in regulated financial contexts. Proficient in leveraging modern MLOps practices and tools to enhance machine learning workflows and model performance.

Highest-signal resume keywords

Machine Learning EngineeringMLOps Tools (MLflow, Kubeflow, Airflow)Python ProgrammingModel Monitoring and VersioningCloud Environments (AWS, GCP, Azure)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringModel EvaluationA/B TestingAlgorithmsData StructuresDistributed ComputingSecure Data HandlingContinuous Delivery EcosystemGenerative AIReinforcement Learning

Soft Skills

Strong Communication SkillsCollaboration in Cross-Functional TeamsMentoring Junior Engineers

Tools & Technologies

TensorFlowPyTorchScikit-learnXGBoostDockerKubernetesCI/CD PipelinesSparkKafkaTerraform

Industry Keywords

FintechE-commerceBankingFinancial DataCompliance

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSAzureBigQueryCloudDockerGoogle Cloud PlatformKafkaKubernetesPythonPyTorchScikit-LearnSparkTensorflowTerraform

About the role

Key responsibilities & impact

Design, build, and optimize AI/ML pipelines for real-time and batch inference using modern MLOps practices
Collaborate with data engineers and software developers to integrate models into TymeX's banking platform, ensuring reliability, monitoring, and version control
Research, prototype, and productionize models for credit scoring, fraud detection, transaction classification, personalization, and conversational AI
Implement model evaluation, A/B testing, and drift detection frameworks to ensure accuracy and stability over time
Contribute to internal frameworks and libraries to standardize ML development workflows across teams
Explore and evaluate emerging techniques in LLMs, Generative AI, and reinforcement learning for TymeX's ecosystem
Mentor junior engineers and collaborate with product and infrastructure teams to ensure model readiness for global scale
Build production ML systems that customers interact with directly across Tyme Group's digital banking platforms

Requirements

What you’ll need

5+ years of hands-on experience in machine learning engineering, data science, or related software development roles
Proven experience deploying and maintaining ML models in production environments, preferably in fintech, e-commerce, or large-scale consumer products
Strong proficiency in Python and core ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.)
Solid understanding of algorithms, data structures, and distributed computing concepts
Experience with MLOps tools such as MLflow, Kubeflow, Airflow, Docker, Kubernetes, and CI/CD pipelines
Familiarity with data engineering stacks such as Spark, Kafka, and cloud data warehouses such as BigQuery and Redshift
Strong understanding of model monitoring, versioning, and observability in live systems
Experience with cloud environments (AWS, GCP, or Azure) and IaC (Terraform, CloudFormation) is a plus
Familiarity with secure data handling and compliance within regulated environments, such as banking or financial data, is advantageous
A system-thinking approach to ML development, viewing models as part of a continuous delivery ecosystem
Curiosity for applied AI research balanced with pragmatism for production constraints
Strong communication skills and ability to collaborate in cross-functional and multi-country teams

Benefits

Comp & perks

Opportunity to build large-scale AI systems that power global digital banking across multiple markets
Work in a cloud-native, event-driven, and API-first architecture with cutting-edge technologies
Hybrid working model
Strong engineering culture
Continuous learning environment (LLMs, MLOps, data observability, and more)
Be part of a global technology organization with shared ownership and impact