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# AI Engineer

- **Company:** Optiver US LLC
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $200,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Frameworks, User Authentication, Automation of Tests, Code Review, Software Design Patterns, Distributed Systems, Fault Tolerance, Python (Programming Language), Object-Oriented Software Development, Software Architecture, Software Maintenance, Software Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Software Application Programming, AI Platforms, Information Technology, Low Latency, Data Management, Automation Anywhere
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a35c3a3f5f80680f

## About the Role

You'll join a culture of collaboration and excellence, surrounded by curious thinkers and creative problem-solvers. Motivated by continuous improvement, you'll thrive in a supportive, high-performing environment while tackling some of the toughest technical challenges in the financial markets., * Bachelor's degree in Computer Science, Artificial Intelligence, or a related technical field

 * 6+ years of professional software engineering experience, including extensive production experience developing applications in Python
 * Strong software engineering fundamentals, including software architecture, object-oriented design, design patterns, distributed systems, testing, and building scalable, resilient applications
 * Experience designing, deploying, and operating production LLM applications, with practical knowledge of model evaluation, prompt engineering, tool calling, context management, agent orchestration, and common failure modes
 * Experience defining evaluation frameworks and using quantitative metrics to guide model selection, prompt iteration, and production readiness
 * Experience integrating AI systems with external tools, APIs, Model Context Protocol (MCP), and modern AI application frameworks
 * Strong engineering judgment, with the ability to determine when AI is the right solution-and when traditional software or deterministic automation is more appropriate
 * Experience building highly reliable production systems where correctness, resiliency, observability, and operational excellence are essential
 * Strong product mindset, with the ability to translate ambiguous business problems into practical technical solutions that deliver measurable business outcomes
 * Excellent communication and collaboration skills, with experience partnering directly with cross-functional teams and end users to refine requirements, challenge assumptions, and deliver scalable solutions
 * Strong problem-solving skills and the ability to thrive in a fast-paced, dynamic environment

## Description

* Design, build, and deploy production-grade AI systems and products-including agent platforms, AI assistants, code review harnesses, evaluation frameworks, and agentic research pipelines-using Python and modern AI frameworks integrated with internal systems, data platforms, and third-party AI services.
 * Evaluate foundation models, agent architectures, and AI workflows, making pragmatic technology decisions based on capability, reliability, latency, security, cost, and business impact.
 * Define success metrics and build robust evaluation frameworks, benchmarking tools, and observability capabilities to measure AI quality, safety, performance, and production readiness while enabling continuous improvement.
 * Translate ambiguous business challenges into scalable technical solutions, ensuring AI-assisted development produces reliable software that meets both functional requirements and business objectives.
 * Design resilient, distributed AI systems with a focus on scalability, state management, authentication, fault tolerance, security, and operational reliability.
 * Rapidly prototype and experiment with emerging models, tools, and workflows, validating new capabilities against measurable success criteria before investing in production.
 * Partner closely with trading, research, and business teams to understand user needs, challenge assumptions, rapidly iterate on solutions, and evolve successful use cases into reusable platform capabilities.
 * Deliver high-quality, maintainable software through sound software engineering practices, automated testing, and thoughtful code reviews.
 * Operate, monitor, and continuously improve production AI systems by optimizing quality, reliability, latency, cost efficiency, governance, and user outcomes.


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