Machine Learning Engineer
Core
Deploying, maintaining, and monitoring AI/ML systems and large language models (LLMs) to power a platform.
Role type
Senior Machine Learning Engineer (MLOps)
Builds
Scalable, production-grade AI solutions and operationalized LLMs
Domain
Artificial Intelligence / Machine Learning / Cloud Infrastructure
Deliverable
production ML models
Required skills
ML deployment pipelines, LLM operationalization, model monitoring & drift detection, CI/CD for ML, cloud platform optimization, containerization & orchestration
Preferred skills
Python, PyTorch/TensorFlow, ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI)
Technologies
Docker, Kubernetes, AWS, GCP, Azure, Python, PyTorch, TensorFlow, MLflow, Kubeflow, SageMaker, Vertex AI
Responsibilities
Design and maintain ML deployment pipelines for scalable production systems; Operationalize LLMs and AI/ML models ensuring high availability; Build robust model monitoring, logging, and alerting systems; Partner with data scientists to transition models from research to production; Develop CI/CD pipelines for ML workflows; Optimize runtime performance of ML models across cloud platforms; Apply containerization and orchestration for reproducible, scalable systems
Seniority
Senior, hands-on IC
