Applied AI Engineer (ai Adoption)

Jump Trading Jump Trading · Quant · Chicago, IL +1 · Research and Development

Applied AI Engineer to partner with back office teams (Finance, Legal, HR) to integrate AI into their day-to-day work. Build solutions using internal AI platform, own full lifecycle from discovery to deployment, and establish best practices for prompting, agent design, evaluation, robustness, and responsible AI use.

What you'd actually do

  1. serve as the primary technical partner for Jump’s back office teams, including Finance, Legal, HR, and other non-trading functions, as they integrate AI into their day-to-day work.
  2. pair with developers and AI champions across the firm to identify high-value use cases, build solutions using Jump's internal AI platform, and work closely with R&D and infrastructure to unlock new capabilities as needed.
  3. own the full lifecycle of back office AI solutions, from discovery and stakeholder conversations to prototyping, deployment, and ongoing iteration, while establishing best practices for prompting, agent design, evaluation, robustness, and responsible AI use.

Skills

Required

  • 2+ years of professional software engineering or applied AI experience
  • Strong proficiency in Python, with hands-on experience integrating LLM APIs, building AI-powered applications, or working with agent/automation frameworks
  • Demonstrated ability to work directly with non-technical or semi-technical business stakeholders, translating ambiguous problems into clear, actionable solutions
  • Excellent communication and interpersonal skills
  • Comfort explaining complex technical concepts to diverse audiences and building trust across teams
  • Strong analytical and problem-solving skills with a bias toward simplicity and usability
  • Familiarity with core software engineering fundamentals
  • Ability to work smoothly across a range of technical environments (Mac, Windows, and Linux) as needed
  • Bachelor's degree in Computer Science, Computer Engineering or related field
  • Reliable and predictable availability

What the JD emphasized

  • primary technical partner
  • own the full lifecycle
  • establishing best practices for prompting, agent design, evaluation, robustness, and responsible AI use
  • technical depth
  • product intuition
  • genuine curiosity
  • follow new patterns closely as they emerge
  • experiment often
  • responsible, secure, and scalable framework

Other signals

  • integrating LLM APIs
  • building AI-powered applications
  • agent/automation frameworks
  • responsible AI use
  • establishing best practices for prompting, agent design, evaluation, robustness