Forward Deployed Engineer

Praxent is an Anthropic partner, and the opportunity in front of us is unlike anything before. Every financial institution is asking the same question at once: how do we put agentic AI to work inside walls this critical? Very few teams have both the frontier-model access to build the answer and the regulatory depth to get it approved. We have both.

That depth is 26 years in the making. Praxent builds the software that banks, credit unions, lenders, insurers, and wealth platforms actually run, inside the hardest environments in enterprise software. Most firms treat those constraints as obstacles. We've built our craft around them.

These roles have been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice which must be identified to the Company. Employees may live in the following locations: Texas, Colorado, Florida, Georgia, Massachusetts, Maryland, Minnesota, Nebraska, New York, New Jersey, North Carolina, Oregon, Pennsylvania, Tennessee, South Carolina, Washington.

Applicants for this position must be currently and legally authorized to work in the United States without the need for current or future sponsorship (e.g., H-1B, J-1, F-1, CPT, OPT, etc.). Praxent will not offer immigration sponsorship or assume sponsorship of an employment visa for this position. International relocation or remote work arrangements outside of the U.S. will not be considered.

The mission

As a Forward Deployed Engineer, you embed directly with a Praxent client (a bank, lender, or payments company) and build the AI systems that run inside their business. An Architect or Senior FDE sets the shape of the system. You own a workstream inside it and take it to production writing most of the code, making day-to-day calls, and getting your piece across the compliance line.. Not a prototype that impresses in a room, but a system that holds up after we hand it over.

• 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.
• 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.
• 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.
• Systems the client can run without you. The evals, guardrails, runbooks, and handoffs that let their team operate and trust what you built.
• Earned trust on the ground. Client engineers who want you in the room because your communication is clear and your follow-through is reliable.

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.

• 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.

• Care deeply. You treat the client's users, data, and constraints as if they were your own.
• Always deliver. You do what you said you would, and people can count on your follow-through.
• Never settle. You keep raising the quality bar even when 'good enough' would pass review.
• Do it together. You build trust with the client's team and your Praxent peers, and you look for win-win.
• Own the outcome. You take responsibility for results, not just tasks, and you are the first to step toward what is broken.

• A seat at the frontier, inside an industry that matters. Praxent is an Anthropic partner and a Microsoft partner. That means partner-level enablement and direct lines into both ecosystems — applied to banks, lenders, and insurers where the work is hard and the stakes are real.
• Twenty-six years of runway. A reputation in financial services that took decades to earn. Do frontier work on solid ground.
• Real time off. 15 days PTO to start and growing every year, 9 US holidays, 5 wellness days, paid parental leave.
• The standard stuff, done right. Medical, dental, vision, disability, accident, a wellness program, and an IRA with up to 3% match.
• A culture people vouch for. Recognized by Texas Monthly, Clutch, and Comparably. Doors are open, ideas travel upward, and nobody here says "it's just business.”

The US base salary for this full-time position is targeted to be $156,000-$186,000 + benefits. Our ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

#LI-Remote