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

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.

Senior Applied AI Engineer

Key skills