ML Engineer

Job Description

The role is focused on building, deploying, and maintaining production machine-learning pipelines, ensuring reliable, scalable, and observable systems that support client-facing applications.

• 4+ years of software or machine-learning engineering experience, including hands‑on experience deploying models to production.
• Demonstrated track record operationalizing models developed by data scientists into reliable, scalable, and observable production systems.
• Production experience with classical machine-learning workloads – distinct from generative‑AI application development.
• Strong collaboration skills across data science, software engineering, and platform teams.
• Commitment to building reliable, well‑engineered, and maintainable systems.

• Experience with feature stores and large‑scale or streaming data pipelines.
• Knowledge of infrastructure‑as‑code (e.g., Terraform) and cloud cost optimization.
• Experience with workflow orchestration tools (e.g., Apache Airflow).
• Familiarity with model‑serving frameworks and API design for inference.
• Light exposure to generative‑AI or LLM deployment.
• Experience operating systems in a regulated or enterprise environment.
• Cloud or MLOps certifications.

• Build, deploy, and maintain production machine‑learning pipelines, from data ingestion and feature processing through to model serving.
• Operationalize models developed by data scientists, ensuring reliability, scalability, reproducibility, and performance.
• Implement MLOps practices including CI/CD for machine learning, model versioning, automated retraining, and model registries.
• Design and maintain feature pipelines and, where relevant, feature stores.
• Set up monitoring and alerting for model performance, data drift, and system health, and respond to degradation.
• Optimize inference latency, throughput, and resource cost for deployed models.
• Containerize and orchestrate machine‑learning workloads using Docker and Kubernetes on Oracle Cloud Infrastructure (OCI).
• Automate and harden data and model workflows to reduce manual intervention.
• Collaborate with data scientists on model handoff, packaging, and success metrics.
• Work with software engineers to integrate models into client‑facing applications and services.
• Apply software‑engineering best practices including testing, code review, and documentation to machine‑learning systems.
• Troubleshoot and resolve production issues across the model‑serving stack.

• Bachelor's degree in Computer Science, Software Engineering, or a related field; equivalent experience accepted.
• Strong Python engineering skills and production experience with ML frameworks such as scikit‑learn, Tensor Flow, or PyTorch.
• Hands‑on experience with MLOps tooling (e.g., MLflow, Kubeflow) and CI/CD pipelines.
• Proficiency with containerization and orchestration (Docker, Kubernetes).
• Experience building and maintaining data pipelines and processing large datasets.
• Experience with cloud infrastructure, ideally Oracle Cloud Infrastructure (OCI) and OCI Data Science.
• Understanding of model monitoring, drift detection, and retraining strategies.
• Solid software‑engineering fundamentals including testing and version control.