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Remote role, mid-level (3+ years) and broad ML + MLOps skillset increases applicant density.
ML and MLOps skills are transferable across industries, though regulated-document expertise increases domain specificity.
Explicit 3+ years, degree plus mandatory ML, cloud, Docker/Kubernetes and MLOps requirements make filters stringent.
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Own full lifecycle of production AI/ML systems from experimentation to deployment and maintenance.
Train, fine-tune, optimize, and improve performance of machine learning models including LLMs.
Implement MLOps practices including CI/CD, monitoring, automated retraining, and design scalable APIs and data pipelines.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
3+ years experience as an AI Engineer, Machine Learning Engineer, or Applied AI Engineer.
Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn); experience with production ML systems and cloud platforms (AWS, GCP, or Azure).
Experience with MLOps, CI/CD pipelines, Docker, Kubernetes, and API design; familiarity with cloud ML services and some knowledge of PostgreSQL and modern data infrastructure.
Experienced with scalable AI systems and LLMs, comfortable handling end-to-end production ML workflows.
Strong skills in cloud-based ML deployment and infrastructure including MLOps and container orchestration.
Able to collaborate across teams to integrate AI into complex, regulated, document-heavy professional workflows.