Job description

The Role We’re hiring multiple Forward Deployed Engineers to join our AI Solutions team across our US and EMEA offices. You’ll be embedded with the business – working alongside portfolio managers, researchers and corporate teams – to identify, build and productionize AI solutions that take real work off their desks. Where our AI Platforms team builds the shared foundation, you build on top of it and deliver into the business. This is a high-impact, hands-on engineering position where you’ll contribute not just as an AI software engineer, but also as an AI strategist. You’ll work in a dynamic, fast-paced environment where product requirements evolve, and business stakeholders are deeply engaged. We are growing this team through 2026 and expect to make several hires from this posting across both regions. We are hiring at Engineer through Senior Engineer level and will calibrate level, scope and compensation to your experience. What you’ll do You’ll work closely with various business stakeholders, helping shape feature definitions, and design and build high-impact AI-powered solutions to automate manual processes. Design and implement new Agentic AI-powered workflows to run continuously in the background, analyzing market news, security data, SEC filings, emails, and other data sources and surface pertinent information that can help drive investment decisions. Develop agents, skills, prompts, and workflow templates, along with tailored customizations for portfolio managers.Own delivery end-to-end, from requirements through production and post-launch iteration, including User Acceptance Testing with relevant stakeholders until the solution is successful Feed proven patterns back to the AI Platforms team as reusable assets, and contribute to the team’s code quality, best practices and development lifecycle Where you might focus We are filling several of these roles and each one is embedded with a different part of the firm. Depending on your background and interests you might focus on one of the areas below – let us know which is the better fit for you. Investment-facing – embedded with a strategy, building solutions against the investment process alongside portfolio managers and research teams Non-investment and enterprise – partnering with corporate and operational functions to automate document-heavy and manual workflows end-to-end Agentic coding – embedded with quant and engineering teams to drive adoption and effectiveness of agentic coding tools such as Claude Code, solving team-specific problems and turning what works into reusable patterns EMEA – the same remit based in our London office, partnering with investment and non-investment teams in the region What you need: 5+ years of professional software engineering Python experience in finance, working with investment professionals Experience with AWS cloud services (S3, OpenSearch, DynamoDB, etc.), Kubernetes-driven deployments, Github Actions, and modern CI/CD pipelines Experience building and shipping software directly with non-engineering users, gathering requirements from them and iterating on real feedback Comfort working with messy real-world data such as documents, filings, emails, and market and reference data Product sense: gathering requirements, prioritizing features and owning a roadmap, even in an informal or unofficial capacity 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 in finance working directly with investment professionals – required for our investment-facing roles, valued but not essential for the others 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 Hands-on use of agentic coding tools such as Claude Code in your own workflow Product management experience, or exposure to product-management-like responsibilities 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 $200,000 and $250,000. Because we are hiring at more than one level, the offer will be calibrated to the level at which a candidate is hired. 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. __PRESENT

Requirements

Must-haves

What you’ll do
You’ll work closely with various business stakeholders, helping shape feature definitions, and design and build high-impact AI-powered solutions to automate manual processes.
Feed proven patterns back to the AI Platforms team as reusable assets, and contribute to the team’s code quality, best practices and development lifecycle
Where you might focus
Investment-facing – embedded with a strategy, building solutions against the investment process alongside portfolio managers and research teams
Non-investment and enterprise – partnering with corporate and operational functions to automate document-heavy and manual workflows end-to-end
Agentic coding – embedded with quant and engineering teams to drive adoption and effectiveness of agentic coding tools such as Claude Code, solving team-specific problems and turning what works into reusable patterns
EMEA – the same remit based in our London office, partnering with investment and non-investment teams in the region
5+ years of professional software engineering Python experience in finance, working with investment professionals
Experience with AWS cloud services (S3, OpenSearch, DynamoDB, etc.), Kubernetes-driven deployments, Github Actions, and modern CI/CD pipelines
Experience building and shipping software directly with non-engineering users, gathering requirements from them and iterating on real feedback
Comfort working with messy real-world data such as documents, filings, emails, and market and reference data
Product sense: gathering requirements, prioritizing features and owning a roadmap, even in an informal or unofficial capacity
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 in finance working directly with investment professionals – required for our investment-facing roles, valued but not essential for the others
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
Hands-on use of agentic coding tools such as Claude Code in your own workflow
Product management experience, or exposure to product-management-like responsibilities

Nice-to-haves

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Application prep

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Keywords to mirror

aipythonawskubernetesci/cd

Platform: greenhouse
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