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Protocol Intelligence
Data-driven signals on your job's competitivenessRemote mid-level AI role and broad applicant pool increase candidate competition.
Role requires specialized ML/LLM and MLOps expertise, limiting cross-industry portability.
Mandatory ML, MLOps, cloud, LLM, and Kubernetes experience makes shortlisting highly strict.
Job Description
Structured overview of role & requirementsAbout This Role
Own end-to-end AI system delivery from experimentation through production, including training, fine-tuning, and deploying machine learning models, notably LLMs.
Design, build, and maintain scalable AI pipelines, APIs, and services ensuring performance improvements across accuracy, latency, reliability, and cost.
Implement MLOps best practices including CI/CD, monitoring, automated retraining, and collaborate cross-functionally to integrate AI into enterprise workflows.
Minimum Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
Minimum 3 years experience in AI/ML engineering roles involving model training, fine-tuning, and production deployment.
Strong proficiency with Python and ML frameworks like PyTorch, TensorFlow, scikit-learn; experience with cloud platforms (AWS, GCP, Azure) and ML services (SageMaker, Vertex AI).
Experience operating production ML systems, containerization (Docker, Kubernetes), MLOps practices, and knowledge of distributed architectures; remote work location.
Ideal Candidate Profile
Experienced in designing and scaling LLM-powered AI systems within regulated, document-heavy professional workflows or enterprise environments.
Skilled in building robust MLOps pipelines and infrastructure to support continuous integration, deployment, and monitoring of production AI models.
Competent in collaborating across product and engineering teams to embed AI capabilities into customer-facing applications with focus on security, privacy, and reliability.