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Strong Tier-1 brand, entry-level AI role, and broad GenAI applicant pool increase competition.
Core ML/MLOps skills are transferable but financial domain preference increases specificity.
Requires specific ML/MLOps, GCP, and GenAI tool skills but no strict numeric experience requirement.
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Build, test, and deploy production-ready generative AI applications and convert prototypes into stable enterprise services.
Develop and maintain backend pipelines for generative AI including RAG systems, multi-agent frameworks, and semantic search under senior guidance.
Support MLOps by building CI/CD pipelines, setting up logging, tracing, monitoring, and optimizing model inference on GCP for low latency and high throughput.
Entry-level position with software development skills in Python and backend engineering (APIs, microservices).
Hands-on experience with GCP (Cloud Run, GKE/Kubernetes, BigQuery) or equivalent cloud platforms and familiarity with Git and CI/CD pipelines.
Practical exposure to foundation model APIs like OpenAI or Anthropic, and familiarity with GenAI frameworks (LangChain, LlamaIndex, Google ADK) is preferred.
Work Experience Required: Not explicitly mentioned in the JD.
Comfortable working in cloud environments and applying DevOps practices focused on scalable AI systems on GCP.
Capable of collaborating across teams including math experts, MLOps architects, and business stakeholders to translate ideas into software.
Demonstrates a strong analytical mindset and interest in learning commodity and financial derivative markets relevant to CME Group.