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.
Develop 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.
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.
Familiarity with agent frameworks like Autogen or LangChain is a bonus.
Yearly based
Worldwide
United States
