Builds and productionizes AI platform components including LLMs, agentic frameworks, and core agent capabilities. Requires 3-6+ years backend experience with Python, Golang, AWS, Kubernetes, and AI-native product shipping.
Salary not listed
On-site3+ YOEML Engineering
About the role
What You’ll Do
Drive the end-to-end execution and delivery of AI-native products, moving from initial prototype to scalable production services with high velocity.
Architect, build, and productionize core components of our AI platform – including:
LLMs and the surrounding ecosystem (e.g., vector DBs, prompt tuning, etc.)
Agentic frameworks
Core agent capabilities like MCP/ cross-platform tool use, web browsing, computer use
Enforce technical discipline and operational excellence for all of our AI products.
Rapidly iterate on AI products based on performance metrics and user feedback to drive continuous improvement.
Lead the evaluation and adoption of new AI technologies and frameworks to keep Addepar on the frontier of the industry.
Partner with product managers and other engineering teams to translate strategic goals into technical realities, abstracting AI complexity so they can iterate quickly.
Who You Are
3-6+ years of professional experience in backend software engineering, with expertise in programming languages/technologies like Python, Golang, gRPC, AWS, and Kubernetes.
Proven track record of owning and shipping complex, production-grade systems.
Proven experience shipping and maintaining AI-native products or features, or demonstrable passion through significant personal projects in AI.
Deep ownership mindset, seeing projects through to completion and raising the bar for execution across the team.
A strong bias for action, with a passion for shipping rapidly and iterating with feedback.
BS/MS in Computer Science, Statistics, Mathematics, or another quantitative field, or equivalent experience.
Bonus Points (Preferred Qualifications)
Specific experience with LLMs and agentic systems.
Hands-on experience with technologies in the modern AI/LLM ecosystem, such as Databricks, Langchain, or MLFlow.
Advanced understanding of the probabilistic systems underpinning modern LLMs.
Experience building AI products in highly accuracy-sensitive domains such as finance is a plus.
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