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Bretton AI

Forward Deployed Engineer

Forward Deployed Engineer deploys AI agents for financial services customers, handles onsite integrations in regulated environments, builds connectors and frameworks, and translates customer needs into product insights. Requires 2+ years software engineering with Python, APIs, data pipelines, cloud, and AI experience.

150k – 200kSan Francisco, CAML EngineeringOnsite2+ YOE
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About the role

What You'll Do

  • Deploy Bretton's AI agents at banks, fintechs, and payments companies, navigating the security and compliance requirements that come with regulated environments.
  • Travel onsite to work through last-mile connectivity challenges, from wiring up core banking integrations to resolving edge cases that only surface in live production.
  • Partner with BSA officers, AML analysts, and risk teams to translate their workflows and regulatory requirements into agent behavior that reflects how their business actually works.
  • Build the connectors, configuration tools, and internal frameworks that turn one-off customer solutions into repeatable platform capabilities.
  • Guide customers from demo through go-live as a trusted technical advisor, helping them resolve issues quickly and expand the value they get from Bretton.
  • Work cross-functionally with engineering, product, and GTM to remove blockers and do whatever it takes to make customers successful.
  • Diagnose and resolve issues in real deployments by reproducing problems and collaborating with the engineering team to fix them.
  • Translate customer usage into product insights by sharing customer needs with engineering and product teams and identifying opportunities to turn recurring solutions into product features.

Technical Qualifications

  • 2+ years of experience as a software engineer or similar
  • Strong backend or data engineering background
  • Experience with Python, APIs, data pipelines, and cloud infrastructure
  • Previous experience in a customer-facing technical role (e.g., solutions engineer, forward deployed engineer, or technical implementation role) is a plus
  • Actively building with LLMs and modern AI systems is a must

Soft Skills

  • Excellent communication skills
  • Ability to explain AI systems to non-technical stakeholders
  • Strong debugging and problem-solving mindset
  • Comfort working with enterprise customers

Compensation & Benefits

  • $150k - $200k base salary + meaningful equity
  • Comprehensive healthcare, 401k matching, commuter benefits
  • 15 days PTO + holidays, unlimited sick days
  • Flexible leave options
  • Working late? We cover DoorDash and Uber home

Skills

PythonAPIsData PipelinesCloud InfrastructureLLMsAi SystemsBackend EngineeringData Engineering

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