Machine Learning Operations Lead
Office Location: UnionBank Plaza, Ortigas, Pasig City
Employment Type: Full-time
About The Role
The MLOps Lead is responsible for driving the end‑to‑end operationalization of machine learning solutions, ensuring they are scalable, secure, and production‑ready. This role provides technical leadership in designing and implementing ML pipelines, CI/CD workflows, feature management, and model deployment frameworks. The MLOps Lead collaborates closely with Data Science, Data Engineering, and DevOps teams to establish standards, streamline model delivery processes, and maintain high‑performance ML systems. By overseeing automation, infrastructure, and governance across the ML lifecycle, the role enables the organization to accelerate model development, improve system reliability, and achieve strategic business outcomes.
What You'll Do
- Lead the design, development, and maintenance of end‑to‑end machine learning pipelines, ensuring reliability, scalability, and production readiness.
- Oversee the deployment, monitoring, and ongoing optimization of machine learning models in production environments.
- Build, enhance, and govern CI/CD workflows to automate model training, testing, and deployment processes.
- Guide the implementation and management of feature stores to support efficient feature engineering and reuse across ML projects.
- Collaborate closely with DataOps, Data Engineering, and DevOps teams to ensure seamless data ingestion, processing, storage, and ML system integration.
- Drive infrastructure improvements involving containerization, orchestration, and cloud-based ML environments using tools such as Docker, Kubernetes, AWS, and Azure.
- Ensure ML solutions comply with organizational standards for security, reliability, scalability, and operational governance.
- Provide technical leadership, mentorship, and architectural guidance to the ML engineering team, promoting best practices and operational excellence.
- Troubleshoot and resolve performance, integration, and production issues related to ML systems.
What We're Looking For
- Expertise in Python and strong software engineering fundamentals. Strong knowledge of SQL and experience with large‑scale databases.
- Deep hands‑on experience with MLOps frameworks (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
- Advanced skills in DevOps: CI/CD, GitOps, Docker, Kubernetes.
- Solid experience deploying and monitoring ML models in production environments.
- Strong working knowledge of cloud environments (AWS or Azure).
- Proficiency in Snowflake and integrating data platforms with ML p
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