Job description

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 Our Culture The firm’s ethos is embedded in our people. ‘Talent is our strategy’ is our mantra and drives how we approach all initiatives at the firm. We believe our success is because of our people, so putting our talent above all else is our top priority. Schonfeld strives to create an environment where our people can thrive. We foster a teamwork-oriented, collaborative environment where ideas at any level are encouraged and shared. The development and advancement of our talent is honed through interactions with each other, learning & educational offerings, and through opportunities to make impactful contributions. At Schonfeld, we strive to cultivate a sense of belonging throughout all of our employees with Diversity, Equity and Inclusion at the forefront of this mission. As a firm we are committed to creating a hiring process which is not only fair, but also welcoming and supportive. On a daily basis, our employees welcome diversity across identity, thought, people and views which serves as the foundation of our culture and success. You can learn more about our DEI initiatives here - Belonging @ Schonfeld. Who we are Schonfeld Strategic Advisors is a multi-manager platform that invests its capital with Internal and Partner portfolio managers, primarily on an exclusive or semi-exclusive basis, across four trading strategies; quantitative, fundamental equity, tactical trading and discretionary macro & fixed income. We have created a unique structure to provide global portfolio managers with autonomy, flexibility and support to best enable them to maximize the value of their businesses. Over the last 30 years, Schonfeld has successfully capitalized on inefficiencies and opportunities within the markets. We have developed and invested heavily in proprietary technology, infrastructure and risk analytics and continue to capitalize on new opportunities. In 2021 we launched our newest strategy, discretionary macro & fixed income as part of the continual growth of Schonfeld’s investible universe. Our portfolio exposure has expanded across the Americas, Europe and Asia as well as multiple asset classes and products . 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.

Requirements

Must-haves

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.
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
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonrestpostgresawskubernetesci/cd

Platform: greenhouse
Effort: quick