MLOps Engineer

2+ years of experience working in ML operations, ML engineering, or related infrastructure roles. Familiarity with deploying ML models and automating ML pipelines. Comfort working with AWS (or similar cloud environments), Docker, and Kubernetes. Experience with workflow orchestration tools like Airflow, Dagster, or Kubeflow is a plus. Strong Python development skills. Solid understanding of software engineering practices (testing, logging, version control, code review). Experience with tools such as MLflow, SageMaker, TensorFlow Serving, or TorchServe. Bonus: hands-on experience implementing model monitoring or drift detection systems. Comfortable working cross-functionally with technical and non-technical stakeholders. Curious, communicative, and open to feedback. Willing to learn from others and share what you know. Excited about learning the ins and outs of ML systems in production. Bring energy, ownership, and a desire to build things that are both elegant and effective.

Build and maintain robust ML pipelines for training, evaluation, and deployment. Automate routine workflows and support reproducible, auditable experimentation. Package and deploy models into production environments. Build REST/gRPC services to serve models. Implement systems to monitor model health in production, detect drift, and log predictions. Contribute to alerting and dashboarding for deployed models. Work within CI/CD systems to support model validation, promotion, and rollback. Partner with ML Engineers and Data Scientists to bring ML systems into production. Contribute to shared libraries, improve developer experience, and help debug operational issues. Partner with Infra and DevOps teams to implement ML systems and related cloud architecture.