Applied Cloud and AI Engineer - Equities Cloud Platform Technology

Millennium•New York, NY

9d•$100,000 - $175,000

About The Position

Technology is core to the health and growth of Millennium’s business. The firm’s active, multi-manager model demands flexible, scalable technology and advanced proprietary systems, including the next generation of analytical and trading capabilities. The Equities Platform and Cloud Engineering team operates at the intersection of applied AI, cloud infrastructure and DevOps, supporting production-grade platforms and systems in a fast-moving engineering environment.

Requirements

Bachelor’s or master’s degree in Computer Science, Engineering or a related field, or equivalent practical experience.
Hands-on experience developing LLM applications, AI agents, prompt engineering or retrieval-augmented generation systems.
Experience with agentic AI frameworks such as Google ADK, PydanticAI, Claude Agent SDK or similar technologies, including tool-based architectures, MCP servers, hooks, plugins or skills.
Strong Python software engineering skills and experience building production APIs and services.
At least one year of experience with AWS, Azure or GCP and modern cloud architecture patterns.
At least one year of experience with Docker, Kubernetes, Terraform and CI/CD workflows, with familiarity in deployment tools such as GitHub Actions or ArgoCD.
Familiarity with vector and graph databases, data integration patterns and observability tools such as Grafana, Prometheus or Datadog.
A proactive, creative and ownership-driven approach, with the ability to balance AI experimentation with disciplined engineering, security, access control and production reliability.

Responsibilities

Build and maintain secure, cloud-native infrastructure spanning compute, storage, networking, identity and access management, secrets management, logging and monitoring.
Own CI/CD pipelines, infrastructure as code, containerization and deployment automation for AI and cloud platform services.
Design, build and deploy production-ready LLM applications, multi-step AI agents and agentic workflows using orchestration, tool calling, memory and structured outputs.
Integrate vector databases, graph databases, APIs and internal platforms to enable complex retrieval and automation use cases.
Build Agent Harness infrastructure that manages LLM calls, tool usage, retries, policy enforcement and reusable agent execution patterns.
Develop Agent Flywheel pipelines that capture traces, identify regressions, route failures into evaluation workflows and improve prompts, tools and models using production signals.
Implement evaluation suites and deployment gates that measure task success, tool-selection accuracy, hallucination rates, latency, cost and overall agent quality.
Partner across engineering and product teams to move solutions from prototype to production, prioritizing reliability, scalability, security and operational excellence.