Remote AI Systems Engineer (Client-Embedded)

Praxent; Seattle (WA); Remote; USD 156,000 - 186,000; Full time

Benefits offered by this job

15 days PTO

9 US holidays

5 wellness days

Paid parental leave

Job summary

Praxent is hiring a Forward Deployed Engineer to embed with a client in the banking/payments space, building the AI systems that run inside their environment. You’ll own a workstream, write substantial code, and navigate compliance and risk reviews to production.

You’ll collaborate with Praxent engineers and client teams, bringing 3+ years shipping production software, deep AI expertise (LLMs, agentic systems), and fluency in Python and TypeScript, across AWS/Azure/GCP.

Qualifications

• 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.
• Fluency in Python and/or TypeScript, and comfort across the stack and a major cloud (AWS, Azure, or GCP), containers, and CI/CD.
• Experience embedded within a client environment integrating with real-world systems and data.
• Ability to hold technical conversations with client engineers and work with them.
• Experience in banking, lending, or payments, including risk and compliance involvement.

Responsibilities

• Shipped, dependable AI in the client's environment.
• Agentic and LLM-backed features that meet requirements and handoff, not just in staging.
• End-to-end work stream from understanding the client's problem to delivered, documented solution.
• Integration with legacy cores, governed data, identity and infrastructure, rather than a greenfield.
• Evals, guardrails, runbooks, and handoffs enabling client teams to operate the system.

Skills

LLM & agentic systems

Python

TypeScript

Cloud platforms

Client embedding

CI/CD

Banking/payments domain

Tools

AWS

Azure

GCP

Job description

Praxent is hiring a Forward Deployed Engineer to embed with a client in the banking/payments space, building the AI systems that run inside their environment. You’ll own a workstream, write substantial code, and navigate compliance and risk reviews to production.

You’ll collaborate with Praxent engineers and client teams, bringing 3+ years shipping production software, deep AI expertise (LLMs, agentic systems), and fluency in Python and TypeScript, across AWS/Azure/GCP.