Praxent - Forward Deployed Engineer
Requirements
• 3+ years shipping production software that other people depended on. Direct experience with LLM and agentic systems (RAG, agent frameworks, vector stores, evals) is ideal, though strong product engineers who have recently gone deep on AI are very much in scope. • Fluency in Python and/or TypeScript, and comfort across the stack and a major cloud (AWS, Azure, or GCP), containers, and CI/CD. • A track record of being directly embedded within a client or customer's environment integrating with messy, real-world systems and data, not just building from scratch. You are working within decisions someone else has made. • The ability to hold a technical conversation with a client’s engineers and work directly with them. • Experience in banking, lending, or payments is strongly preferred, including working directly with risk and compliance partners and getting AI through model-risk or security review in a regulated organization. • How you work: our CAN/DO values in practice • Care deeply. You treat the client's users, data, and constraints as if they were your own. • Care deeply. • Always deliver. You do what you said you would, and people can count on your follow-through. • Always deliver. • Never settle. You keep raising the quality bar even when 'good enough' would pass review. • Never settle. • Do it together. You build trust with the client's team and your Praxent peers, and you look for win-win. • Do it together. • Own the outcome. You take responsibility for results, not just tasks, and you are the first to step toward what is broken. • Own the outcome.
Responsibilities
• Shipped, dependable AI in the client's environment. Agentic and LLM-backed features that meet the client's functional and compliance requirements and keep working after handoff, not just in our staging. • Shipped, dependable AI in the client's environment. • A work stream, end to end. You take a piece of the engagement from understanding the client's problem through to a delivered, documented solution. • A work stream, end to end. • AI that fits their world. Clean integration with the systems you are actually given, including legacy cores, governed data, and their identity and infrastructure, rather than a greenfield ideal. • AI that fits their world. • Systems the client can run without you. The evals, guardrails, runbooks, and handoffs that let their team operate and trust what you built. • Systems the client can run without you. • Earned trust on the ground. Client engineers who want you in the room because your communication is clear and your follow-through is reliable. • Earned trust on the ground. • How we work • How we work • Praxent runs on a spirit of service that our clients feel from the first week. We lead with 'yes, and': we start from the client's goals and shape ourselves around how they actually work, instead of arriving with a fixed playbook. • In financial services, that means treating constraints like compliance, risk reviews, legacy systems, and audit and explainability requirements not as obstacles to route around, but as the design problem itself. The strongest forward-deployed work embraces those constraints and is better for them. • You will not do this alone. You work shoulder to shoulder with Praxent Senior Forward Deployed Engineers and Product Managers and the client's own people, and you own your part of the result from first commit to confident handoff.
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