ClanX

Senior Applied AI Engineer- Remote

Vaga remota de Applied AI Engineering com fit claro de localização do candidato.

Publicada13 de jul. de 2026

Países elegíveisGlobal

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Global

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
3+ years of experience as an AI Engineer, Machine Learning Engineer, Applied AI Engineer, or similar role.
Strong experience training, fine-tuning, and deploying machine learning models to production.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
Experience operating and maintaining production ML systems.
Hands-on experience with AWS, GCP, or Azure cloud platforms.
Familiarity with cloud ML services such as SageMaker, Vertex AI, or similar platforms.
Strong understanding of API design and distributed system architecture.
Experience implementing MLOps practices, CI/CD pipelines, and model monitoring.
Experience with Docker and Kubernetes.
Knowledge of PostgreSQL and modern data infrastructure.
Experience with LLMs, RAG systems, and vector databases is a strong plus.
Excellent written and verbal communication skills in English.

Own end-to-end delivery of production AI and ML systems from experimentation to deployment.
Train, fine-tune, and optimize machine learning models, including LLMs and open-weight models.
Build and maintain training, data processing, and inference pipelines.
Improve model performance across accuracy, latency, reliability, and cost.
Implement MLOps best practices for deployment, monitoring, CI/CD, and automated retraining.
Develop evaluation frameworks, benchmark datasets, and quality checks for production models.
Design and maintain scalable APIs and services that expose AI capabilities.
Collaborate with Product, Backend, and Frontend teams to integrate AI into customer-facing workflows.
Monitor production systems and continuously improve model and infrastructure performance.
Research and evaluate emerging AI techniques, tools, and frameworks.