ML Engineer

Must Have

• 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.

Nice to Have

• 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, TensorFlow, 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.