Job Description:

Responsibilities:

• Deploy and maintain machine learning models in production with scalable endpoints.
• Build and automate data pipelines to preprocess, transform, and feed ML systems.
• Set up high-performance compute clusters for model training and experimentation.
• Monitor ML model performance, detect drift, and trigger retraining as needed.
• Collaborate with engineers to integrate ML workflows into CI/CD pipelines.
• Optimize resource usage (e.g., GPUs, cloud instances) for cost-effective AI operations.
• Develop custom scripts and tools to streamline ML lifecycle tasks.
• Troubleshoot and resolve issues in data flows, model deployment, and infrastructure.
• Document technical processes for the team—clear, concise, and engineer-focused.

Preferred Qualifications:

• Bachelor’s degree in Computer Science, Data Science, or equivalent technical experience.
• 3+ years in MLOps, DevOps, or a related engineering role with ML exposure.
• Experience deploying ML models using frameworks like TensorFlow, PyTorch, or ONNX.
• Proficiency in scripting (Python preferred) and automation tools for data/ML workflows.
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for ML workloads.
• Knowledge of cloud platforms (AWS, Azure, GCP) and ML services (e.g., SageMaker, Vertex AI).
• Hands-on experience with data pipeline tools like Apache Airflow or Kubeflow.
• Strong problem-solving skills and ability to thrive in a remote, all-engineer team.
• Bonus: Experience with ML monitoring tools (e.g., MLflow) or certifications like AWS Machine Learning Specialty.