About this role
Direct client engagement drives the work, as engineers partner with strategic organizations to discover where AI transforms daily operations and build those capabilities from scratch. Team members spend their days understanding unique client workflows, prototyping solutions, and collaborating with core engineering groups to ship general products for all platform users. As part of a newly formed Applied AI group at Addepar, this position exists to set technical direction, handle the most complex client engagements, and establish shared agent patterns and evaluation frameworks.
The right candidate thrives in ambiguous environments where problems remain largely unsolved and high-stakes calls determine production readiness. Someone who thinks in platforms rather than isolated features will succeed here by taking capabilities from zero to one and mentoring fellow engineers.
What you will do
- Set the technical direction for AI engineering across the team by defining agent architecture patterns, evaluation methodology, and deployment strategies.
- Design the shared AI platform layer, including agent frameworks, tool integrations, and evaluation infrastructure.
- Collaborate directly with clients on complex engagements to identify new problem domains and design AI capabilities for previously unautomated workflows.
- Make high-judgment calls on AI readiness to determine production-worthiness and balance speed with reliability.
- Drive the handoff from prototype to product by working with core engineering teams to generalize client-specific capabilities.
- Lead production quality for AI systems through observability design, SLO establishment, and operational practices.
- Mentor and grow AI and full-stack engineers while establishing technical and cultural standards for the new team.
- Shape product direction by translating insights from client engagements into roadmap priorities.
What you need
- Bring six or more years of professional software engineering experience.
- Possess professional experience using Python as a primary programming language.
- Demonstrate deep experience building and operating production AI and LLM-powered systems, including agent architectures and multi-model orchestration.
- Show a track record of designing technical systems that other engineers build on with a platform-oriented mindset.
- Have experience with AI-assisted development tools such as Claude Code or Codex.
- Demonstrate repeated experience taking products or capabilities from zero to one while identifying correct problems and building systems.
- Possess experience working directly with customers or external stakeholders to scope and deliver technical work.
- Hold a Bachelor or Master of Science degree in Computer Science, Mathematics, or a quantitative field, or equivalent experience.
Nice to have
- Experience designing multi-service AI platform architectures including model serving and agent orchestration
- Experience with additional backend languages such as Java
- Financial services domain knowledge covering portfolios, trading, document management, or regulatory workflows
- Experience with production observability and SaaS operational excellence at scale
- Background in developer experience or internal platform work
Benefits
- Base salary range of $158,000 to $248,000 USD
- Bonus
- Equity
- Benefits
Skills and tech stack
Questions candidates ask
What is the remote work policy?
The role is listed as Remote, USA.
What is the compensation range for this position?
The target base salary range is $158,000 to $248,000 USD, which includes base salary alongside bonus, equity, and benefits.
What does the team actually work on?
The Applied AI team works embedded with strategic clients to identify where AI can change their work and builds those capabilities from scratch before partnering with core engineering teams to ship them as products.
What background is required for this role?
Candidates need six or more years of professional software engineering experience, professional Python experience, and deep experience building and operating AI and LLM-powered systems in production.