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About the job

The Role

We’re looking for a Site Reliability Engineer to join our AI Technology team and play a critical role in ensuring the stability and scalability of the firm’s internal Agentic AI platform. This is a high-impact, hands-on engineering position where you’ll contribute code, automation workflows, observability, metrics, and support users as issues arise. You’ll work in a dynamic, fast-paced environment where product requirements evolve, and business stakeholders are deeply engaged.

What you’ll do

You will set the reliability standards for Enterprise AI, defining Service Level Objectives (SLOs), error budgets, and custom incident response runbooks.

You will own the observability, incident response, reliability, and scalability of our AI platform.

Ensure that our agents, gateways, LLM proxies, and RAG pipelines operate with high availability, accuracy, and financial efficiency.

Support users in a dedicated help channel when issues arise — investigating root causes, providing solutions, monitoring the status of upstream dependencies, and communicating updates back to users.

You’ll also be involved in the team’s code quality and best practices, identify gaps in development lifecycles, and continuously improve both the core platform and how developers build with it.

What you need:

5+ years of professional cloud automation or site reliability engineering experience, or similar

Familiarity with software development best practices and experience creating applications in Python

Experience working creating and integrating REST APIs, and event-driven and asynchronous architectures

Experience working with both relational (Postgres, MySQL) and NoSQL databases (DynamoDB, Elastic Search)

Experience with AWS cloud services (S3, OpenSearch, DynamoDB, etc.), Kubernetes-driven deployments, Github Actions, and modern CI/CD pipelines

A passion for AI, creative ideas for how it can be a tool for problem solving and productivity

A thoughtful approach to problem-solving; ability to break down complex issues methodically

Excellent communication skills; Able to translate technical concepts for non-technical stakeholders

We’d love if you had:

Experience developing agentic AI applications, harnesses, MCP servers, and workflows

Familiarity with or experience building AI agents, Retrieval-Augmented Generation (RAG) pipelines, or agentic tools

Experience with observability tooling such as DataDog

Who We Are

Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.

Our Culture

At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.

The base pay for this role is expected to be between $150,000 and $180,000. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.

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