The opportunity

📍Contribute to developing AI agents for task automation and orchestration. 📍Assist in designing systems for intelligent task planning and human-AI collaboration. 📍Support the implementation of workflows for efficient task routing across AI models and human operators. 📍Build and maintain monitoring systems to track agent performance and workflow efficiency. 📍AI/ML pipelines for model training, evaluation, and monitoring in production. 📍Collaborate with SREs and cross-functional teams to ensure optimal AI platform performance. 📍Work with senior engineers to identify and address AI/ML opportunities for improving scalability and efficiency in operations. 📍Stay informed on AI/ML advancements, contributing to team knowledge and fostering innovation in the crypto industry.

Skills you should HODL

📍Experience building, fine-tuning, and deploying ML systems in production , including LLMs and RAG systems. 📍Strong applied ML fundamentals and proficiency in AI algorithms. 📍Hands-on experience with post-deployment tasks such as monitoring, rollouts, and re-tuning. 📍Proficiency with workflow orchestration tools like Prefect, Airflow, or Argo Workflows. 📍Familiarity with deploying web services using container orchestration tools such as Kubernetes (K8s). 📍Experience with ML lifecycle tools (e.g., MLFlow, Kubeflow) and inference servers like Triton, TGI, or vLLM. 📍Proven ability to build reliable CI/CD pipelines for services and model deployments. 📍Strong Python programming skills with expertise in software engineering principles to build scalable systems. 📍Strong problem-solving mindset and effective communication skills for cross-team collaboration.