Senior Machine Learning Engineer

GECO Asia Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

GECO Asia Pte Ltd in Singapore seeks a Senior AI/ML Engineer (MLOps) to design, deploy, monitor, and improve large-scale AI solutions in production, focusing on reliability, governance, and operational excellence of Generative AI and traditional ML systems.

You will collaborate with data scientists, platform teams, and business stakeholders to optimize deployments, monitor model performance, data quality, and latency, and implement observability and CI/CD pipelines using Kubernetes, Docker, and

Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology, or related field.
• 5+ years of experience in Machine Learning, AI Engineering, MLOps, or related software engineering roles.
• Experience supporting production AI/ML systems in cloud environments and agile delivery teams.

Responsibilities

• Design and implement monitoring, alerting, and observability solutions for ML/AI systems, including model performance, data drift, latency, and operational health.
• Build and maintain automated evaluation, testing, and validation pipelines for ML models, GenAI workflows, prompts, and agentic systems.
• Investigate and resolve production issues related to model behavior, AI applications, data quality, integrations, and RAG/retrieval pipelines.
• Manage model, prompt, embedding, and vector index lifecycles, including versioning, rollout strategies, and evaluation gates.
• Collaborate with platform and infrastructure teams to optimize AI deployments for scalability, reliability, and cost efficiency.
• Establish feedback loops using user interactions, business metrics, UAT findings, and expert reviews to drive continuous improvement.

Skills

Strong Python expertise

Generative AI solutions

Machine Learning fundamentals

MLOps, orchestration, and model监监?

Kubernetes

Docker

Prometheus

Grafana

Cloud provider experience

Agile environments

RAG, AI agents

Education

Bachelor's degree in Computer Science / Data Science / Engineering / IT

Tools

Kubernetes

Docker

Prometheus

Grafana

Airflow

Kubeflow

MLflow

Job description

We are seeking a Senior AI/ML Engineer (MLOps) to design, deploy, monitor, and continuously improve large-scale AI and machine learning solutions in production. This role focuses on ensuring the reliability, performance, governance, and operational excellence of both Generative AI and traditional ML systems, while collaborating closely with data scientists, engineers, platform teams, and business stakeholders.

Key Responsibilities

• Design and implement monitoring, alerting, and observability solutions for ML and AI systems, including model performance, data drift, latency, and AI operational health.
• Build and maintain automated evaluation, testing, and validation pipelines for ML models, GenAI workflows, prompts, and agentic systems.
• Investigate and resolve production issues related to model behavior, AI applications, data quality, integrations, and RAG/retrieval pipelines.
• Manage model, prompt, embedding, and vector index lifecycles, including versioning, rollout strategies, and evaluation gates.
• Collaborate with platform and infrastructure teams to optimize AI deployments for scalability, reliability, and cost efficiency.
• Establish feedback loops using user interactions, business metrics, UAT findings, and expert reviews to drive continuous improvement.

General Background

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology, or a related field.
• 5+ years of experience in Machine Learning, AI Engineering, MLOps, or related software engineering roles.
• Experience supporting production AI/ML systems in cloud environments and agile delivery teams.

Mandatory Skills

• Strong Python expertise including Pandas, NumPy, and Scikit-learn for model maintenance, automation, and production support.
• Hands-on experience building or supporting production Generative AI solutions, including RAG, AI agents, LlamaIndex, CrewAI, or similar orchestration frameworks.
• Strong understanding of Machine Learning fundamentals, model evaluation techniques, drift detection, bias monitoring, and model performance management.
• Proven experience in MLOps, orchestration, and model monitoring, using tools such as Kubernetes, Docker, Prometheus, Grafana, or cloud-native monitoring platforms to track model performance, data quality, latency, and operational metrics.
• Production experience on at least one major cloud provider (Azure, AWS, GCP).
• Practical experience working in Agile environments (Scrum/Kanban).

Nice-to-Have Skills

• Experience with Apache Spark, Dask, or other distributed data processing frameworks.
• Familiarity with model serving and deployment platforms such as Azure ML Inference, Triton, SageMaker Endpoints, and API frameworks like FastAPI or Flask.
• Experience with orchestration and ML lifecycle tools such as Airflow, Kubeflow, or MLflow.
• Exposure to Infrastructure as Code (IaC) tools such as Terraform.
• Experience mentoring engineers and driving engineering best practices across teams.
• Experience working with Microsoft Azure AI/ML ecosystem, including services such as Azure Machine Learning, Databricks, or related cloud-based AI deployment platforms.