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# Applied Cloud and AI Engineer - Equities Cloud Platform Technology

- **Company:** MILLENNIUM
- **Location:** New York, NY, United States
- **Salary:** $100,000.0 - $175,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Github, Graph Database, Python (Programming Language), Prometheus, Web Services, Datadog, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Grafana, Prompt Engineering, Kubernetes, Information Technology, Deployment Automation, Virtual Agents, Terraform, Docker
- **Published:** August 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2aab273b0fa7c656

## About the Role

* 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.

## Description

* 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.


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